Movement detection script¶

In this script, we process the motion tracking and build a pipeline to automatically detect a movement event within a specific marker (e.g., wrist, head, ...)

These are then used to compare them with manual annotations and evaluate the performance of the algorithm.

Outline

  1. Preparation
  • here we center the data to have (semi)control for individual motion of keypoints
  • we add 0 to the data
  • we calculate natural noise in articulators in no-movement trials to manipulate the measurement error of OpenPose
  • we calculate percentile of wrist movement threshold (20 cm/s) and corresponding thresholds for other articulators
  1. Annotation
  • we annotate movement events based on the dynamic thresholds (with added natural noise)
  • we apply rule-based algorithm to merge events or delete 'fake' events
  • prepare ELAN-like tiers
  1. Creation of ELAN files
  • from df with annotations, we create ELAN-like files

Preparation¶

Folder settings¶

In [1]:
import os
import glob
import numpy as np
import pandas as pd

curfolder = os.getcwd()
print(curfolder)

# data are in curfolder/P0/P0/
datafolder = curfolder + '/TS_motiontracking'
print(datafolder)
folderstotrack = glob.glob(datafolder + '/mt_*')
# get rid of all 'centered'
folderstotrack = [x for x in folderstotrack if 'centered' not in x]
print(folderstotrack)
e:\FLESH_ContinuousBodilyEffort\TS_processing
e:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking
['e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_0_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_18_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_19_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_1_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_20_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_21_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_22_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_23_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_24_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_25_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_26_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_2_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_36_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_37_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_38_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_39_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_3_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_40_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_41_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_42_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_43_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_44_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_4_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_5_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_6_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_7_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_8_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_tpose_0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_10_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_11_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_12_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_13_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_14_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_15_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_16_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_17_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_27_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_28_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_29_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_30_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_31_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_32_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_33_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_35_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_45_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_46_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_47_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_48_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_49_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_50_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_51_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_52_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_53_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_9_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_1_tpose_1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_0_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_10_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_11_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_12_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_13_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_14_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_15_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_16_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_17_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_18_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_1_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_2_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_38_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_39_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_3_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_40_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_41_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_43_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_44_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_45_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_46_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_47_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_48_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_49_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_4_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_50_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_51_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_52_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_5_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_67_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_68_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_69_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_6_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_70_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_71_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_72_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_73_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_74_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_75_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_76_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_77_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_78_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_79_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_7_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_80_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_81_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_82_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_83_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_84_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_85_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_86_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_87_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_88_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_89_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_8_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_90_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_91_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_9_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_100_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_101_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_102_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_103_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_104_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_105_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_106_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_107_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_108_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_109_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_110_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_111_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_112_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_113_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_19_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_20_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_21_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_22_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_23_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_24_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_25_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_26_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_27_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_28_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_29_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_30_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_31_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_32_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_33_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_34_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_35_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_36_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_37_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_53_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_54_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_55_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_56_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_57_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_58_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_59_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_60_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_61_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_62_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_63_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_64_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_65_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_92_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_93_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_94_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_95_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_96_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_97_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_98_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_0_2_99_p1.csv']

Centering keypoints¶

With motion tracking, keypoints are moving as a consequence of other keypoints moving. For instance, I can jump from the ground and all the keypoints will move, despite only the knees being responsible for the movement per se.

To get to individual movements, we will center the keypoints in the following way:

  1. head region
  • head relative to shoulder midpoint
  1. torso region
  • shoulders relative to hip midpoint
  1. arm region
  • elbow, wrist relative to shoulder midpoint
  1. lower body region
  • knee, ankle, heel relative to hip midpoint
  • hip relative to heel midpoint
In [11]:
import scipy.signal

for file in folderstotrack:

    print('working on' + file)  
    # load in the file
    df = pd.read_csv(file)
    
    # get rid of collumns we will not need
    df = df.loc[:, ~df.columns.str.contains('Nose')]
    df = df.loc[:, ~df.columns.str.contains('Toe')]
    df = df.loc[:, ~df.columns.str.contains('speed')]
    df = df.loc[:, ~df.columns.str.contains('vert_vel')]

    ##### HEAD
    # now we want all head coordinates center on Shoulder midpoint (we cant use Neck because Neck often moves with head)

    # create ShoulderMid
    df['ShoulderMid_x'] = (df['RShoulder_x'] + df['LShoulder_x']) / 2
    df['ShoulderMid_y'] = (df['RShoulder_y'] + df['LShoulder_y']) / 2
    df['ShoulderMid_z'] = (df['RShoulder_z'] + df['LShoulder_z']) / 2

    # center head on ShoulderMid
    df['Head_x_c'] = df['Head_x'] - df['ShoulderMid_x']
    df['Head_y_c'] = df['Head_y'] - df['ShoulderMid_y']
    df['Head_z_c'] = df['Head_z'] - df['ShoulderMid_z']

    #### TORSO
    # create HipMid
    df['HipMid_y'] = (df['LHip_y'] + df['RHip_y']) / 2
    df['HipMid_x'] = (df['LHip_x'] + df['RHip_x']) / 2
    df['HipMid_z'] = (df['LHip_z'] + df['RHip_z']) / 2

    # center shoulders on HipMid
    df['LShoulder_x_c'] = df['LShoulder_x'] - df['HipMid_x']
    df['LShoulder_y_c'] = df['LShoulder_y'] - df['HipMid_y']
    df['LShoulder_z_c'] = df['LShoulder_z'] - df['HipMid_z']
    df['RShoulder_x_c'] = df['RShoulder_x'] - df['HipMid_x']
    df['RShoulder_y_c'] = df['RShoulder_y'] - df['HipMid_y']
    df['RShoulder_z_c'] = df['RShoulder_z'] - df['HipMid_z']

    #### ARMS
    # center arm keypoints on ShoulderMid
    df['LElbow_x_c'] = df['LElbow_x'] - df['ShoulderMid_x']
    df['LElbow_y_c'] = df['LElbow_y'] - df['ShoulderMid_y']
    df['LElbow_z_c'] = df['LElbow_z'] - df['ShoulderMid_z']
    df['RElbow_x_c'] = df['RElbow_x'] - df['ShoulderMid_x']
    df['RElbow_y_c'] = df['RElbow_y'] - df['ShoulderMid_y']
    df['RElbow_z_c'] = df['RElbow_z'] - df['ShoulderMid_z']

    df['LWrist_x_c'] = df['LWrist_x'] - df['ShoulderMid_x']
    df['LWrist_y_c'] = df['LWrist_y'] - df['ShoulderMid_y']
    df['LWrist_z_c'] = df['LWrist_z'] - df['ShoulderMid_z']
    df['RWrist_x_c'] = df['RWrist_x'] - df['ShoulderMid_x']
    df['RWrist_y_c'] = df['RWrist_y'] - df['ShoulderMid_y']
    df['RWrist_z_c'] = df['RWrist_z'] - df['ShoulderMid_z']

    #### LOWER BODX
    # center lower body keypoits on HipMid
    df['LKnee_x_c'] = df['LKnee_x'] - df['HipMid_x']
    df['LKnee_y_c'] = df['LKnee_y'] - df['HipMid_y']
    df['LKnee_z_c'] = df['LKnee_z'] - df['HipMid_z']
    df['RKnee_x_c'] = df['RKnee_x'] - df['HipMid_x']
    df['RKnee_y_c'] = df['RKnee_y'] - df['HipMid_y']
    df['RKnee_z_c'] = df['RKnee_z'] - df['HipMid_z']

    df['LHeel_x_c'] = df['LHeel_x'] #- df['HipMid_x']
    df['LHeel_y_c'] = df['LHeel_y'] #- df['HipMid_y']
    df['LHeel_z_c'] = df['LHeel_z'] #- df['HipMid_z']
    df['RHeel_x_c'] = df['RHeel_x'] #- df['HipMid_x']
    df['RHeel_y_c'] = df['RHeel_y'] #- df['HipMid_y']
    df['RHeel_z_c'] = df['RHeel_z'] #- df['HipMid_z']

    # also center hip on HeelMid
    # create HeelMid
    df['HeelMid_y'] = (df['LHeel_y'] + df['RHeel_y']) / 2
    df['HeelMid_x'] = (df['LHeel_x'] + df['RHeel_x']) / 2
    df['HeelMid_z'] = (df['LHeel_z'] + df['RHeel_z']) / 2

    # now we want to center hip on HeelMiD
    df['LHip_x_c'] = df['LHip_x'] - df['HeelMid_x']
    df['LHip_y_c'] = df['LHip_y'] - df['HeelMid_y']
    df['LHip_z_c'] = df['LHip_z'] - df['HeelMid_z']
    df['RHip_x_c'] = df['RHip_x'] - df['HeelMid_x']
    df['RHip_y_c'] = df['RHip_y'] - df['HeelMid_y']
    df['RHip_z_c'] = df['RHip_z'] - df['HeelMid_z']

    # keep only the centered columns and Time, TrialD
    df = df.loc[:, df.columns.str.contains('_c') | df.columns.str.contains('Time') | df.columns.str.contains('TrialID')]

    # collect all columns except Time and TrialD
    mtcols = list(df.columns)
    mtcols.remove('Time')
    mtcols.remove('TrialID')

    # get Time back to sec
    df['Time'] = df['Time']/1000
    # get sampling rate
    sr = 1/np.mean(np.diff(df['Time']))
    
    # prepare cols for speed
    cols = [x.split('_')[0] for x in mtcols]
    colsforspeed = list(set(cols))

    # upper body cols
    upperbodycols = ['Head', 'Neck', 'RShoulder', 'RElbow', 'RWrist', 'LShoulder', 'LElbow', 'LWrist']
    # lower body cols
    lowerbodycols = ['RHip', 'RKnee', 'RAnkle', 'RHeel' 'LHip', 'LKnee', 'LAnkle', 'LHeel']

    # for each unique column, calculate speed
    for col in colsforspeed:
        # get x and y columns
        x = df[col + '_x_c']
        y = df[col + '_y_c']
        z = df[col + '_z_c'] # note that y and z are flipped
        # calculate speed
        df[col + '_speed'] = np.insert(np.sqrt(np.diff(x)**2 + np.diff(y)**2 + np.diff(z)**2), 0, 0)
        # multiply the values by sr, because now we have values in m/(s/sr), and by 1000 to get ms
        df[col + '_speed'] = df[col + '_speed']*sr
        # smooth with savgol
        if any(x in col for x in upperbodycols):
            df[col + '_speed'] = scipy.signal.savgol_filter(df[col + '_speed'], 15, 1)
        elif any(x in col for x in lowerbodycols):
            df[col + '_speed'] = scipy.signal.savgol_filter(df[col + '_speed'], 40, 1) # there is a lot of noise in lower body, so we increase the window size
            
        # if the col contains wrist, we will alco calculate the vertical velocity (z dimension)
        if 'Wrist' in col:
            # calculate speed
            df[col + '_vert_vel'] = np.insert(np.diff(z), 0, 0)
            # multiply the values by sr, because now we have values in m/(s/sr)
            df[col + '_vert_vel'] = df[col + '_vert_vel']*sr
            # smooth with savgol 
            df[col + '_vert_vel'] = scipy.signal.savgol_filter(df[col + '_vert_vel'], 15, 1)

    # get time back to ms
    df['Time'] = df['Time']*1000

    # save df as a new file
    df.to_csv(file.replace('mt_', 'mt_centered_'), index=False)
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In [12]:
# plot all speeds for RKnee in df

import matplotlib.pyplot as plt
import seaborn as sns

sample = df

# get all columns with RKnee
cols = [x for x in sample.columns if 'RKnee_speed' in x]

# plot all columns
for col in cols:
    plt.plot(sample['Time'], sample[col], label=col)

plt.legend()
plt.show()
No description has been provided for this image

Natural noise of articulators¶

Due to motion tracking error, often it is the case that there is a movement noise in the keypoints that seldomly move, while they stay (relatively) motionless. To account for this, before calculating threshold of movement of each keypoint, we will calculate what is the natural noise of keypoints in trials where there is no movement (except hands, which we will ignore)

No-movement trials (excl. arms)¶

In [15]:
# get centered files
nomovefiles = glob.glob(datafolder + '/mt_centered_0_1_*')

# there are files without motion of the body (excl. arms)
foi = ['0_1_p', '_0_p', '_2_p', '_3_p', '_5_p', '_6_p', '_7_p', '_8_p', '_9_p', '_11_p', '_12_p', '_13_p', '_14_p', '_15_p', '_16_p']
noisefiles = [x for x in nomovefiles if any(y in x for y in foi)]
print(noisefiles)

# columns with motionless points
coi = ['Head', 'RHip', 'RKnee', 'RHeel', 'LHip', 'LKnee', 'LHeel']

# for each file, calculate the mean speed, max speed, min speed, sd speed for each column in coi for speed
noise = {}

# concatenate all the files together to one df
allnoise = pd.concat([pd.read_csv(file) for file in noisefiles])

# get all columns with speed
cols = [x for x in allnoise.columns if 'speed' in x]
# keep only those that are in coi
cols = [x for x in cols if any(y in x for y in coi)]

# for each column, calculate the mean, max, min, sd
for col in cols:
    print(col)
    # if the col starts on R or L, we want to concatenate the other side (left or right) and calculate the mean etc. on both together
    if 'R' in col:
        othercol = col.replace('R', 'L')
        name = col.replace('R', '')
        # get the values for both columns
        keytocount = allnoise[col] + allnoise[othercol]

        # get the mean
        print('mean: ' + str(np.mean(keytocount)))
        print('max: ' + str(np.max(keytocount)))
        print('min: ' + str(np.min(keytocount)))
        print('sd: ' + str(np.std(keytocount)))

        # save the values
        values = [np.mean(keytocount), np.max(keytocount), np.min(keytocount), np.std(keytocount)]
        noise[name] = values
    
    elif 'L' in col:
        # we already calculated it so we can skip it
        print('skipping ' + col)

    else:
        name = col
        # get the values for both columns
        keytocount = allnoise[col]

        # get the mean
        print('mean: ' + str(np.mean(keytocount)))
        print('max: ' + str(np.max(keytocount)))
        print('min: ' + str(np.min(keytocount)))
        print('sd: ' + str(np.std(keytocount)))

        # save the values
        values = [np.mean(keytocount), np.max(keytocount), np.min(keytocount), np.std(keytocount)]
        noise[name] = values
['e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_0_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_11_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_12_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_13_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_14_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_15_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_16_p1.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_2_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_3_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_5_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_6_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_7_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_8_p0.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/TS_motiontracking\\mt_centered_0_1_9_p1.csv']
LKnee_speed
skipping LKnee_speed
LHeel_speed
skipping LHeel_speed
RHeel_speed
mean: 5.004875624374936
max: 16.954267438838876
min: 0.5106655303723385
sd: 2.7607815463614553
RHip_speed
mean: 10.326424849596721
max: 54.49077758547286
min: 0.9239982664543706
sd: 9.017960876402839
Head_speed
mean: 4.3243746575386215
max: 18.32696145797376
min: -0.7498397719774741
sd: 2.719665513018643
LHip_speed
skipping LHip_speed
RKnee_speed
mean: 8.812762536028025
max: 33.385013262411306
min: 2.4257931690567514
sd: 4.36462620292907

No-movement trials (arms)¶

In [131]:
# get centered files
nomovefiles = glob.glob(datafolder + '/mt_centered_0_1_*')

# this is file without no motion completely
foi = ['_1_p']
noisefiles = [x for x in nomovefiles if any(y in x for y in foi)]

# columns with motionless points
coi = ['RWrist', 'LWrist', 'RShoulder', 'LShoulder', 'RElbow', 'LElbow']

# for each file, calculate the mean speed, max speed, min speed, sd speed for each column in coi for speed
wnoise = {}

# concatenate all the files together to one df
allnoise = pd.concat([pd.read_csv(file) for file in noisefiles])

# get all columns with speed
cols = [x for x in allnoise.columns if 'speed' in x or 'vert_vel' in x]
# keep only those that are in coi
cols = [x for x in cols if any(y in x for y in coi)]

# for each column, calculate the mean, max, min, sd
for col in cols:
    print(col)
    # if the col starts on R or L, we want to concatenate the other side (left or right) and calculate the mean etc. on both together
    if 'R' in col:
        othercol = col.replace('R', 'L')
        name = col.replace('R', '')
        # get the values for both columns
        keytocount = allnoise[col] + allnoise[othercol]

        # get the mean
        print('mean: ' + str(np.mean(keytocount)))
        print('max: ' + str(np.max(keytocount)))
        print('min: ' + str(np.min(keytocount)))
        print('sd: ' + str(np.std(keytocount)))

        # save the values 
        values = [np.mean(keytocount), np.max(keytocount), np.min(keytocount), np.std(keytocount)]
        wnoise[name] = values
    
    elif 'L' in col:
        # we already calculated it so we can skipit
        print('skipping ' + col)
RWrist_speed
mean: 8.148664198131078
max: 11.740306021401924
min: 4.618764556306746
sd: 1.9990770440761167
RWrist_vert_vel
mean: 0.3149308813912007
max: 3.614311484827629
min: -5.6034372531266925
sd: 2.0154133461692703
LElbow_speed
skipping LElbow_speed
RShoulder_speed
mean: 10.854913809687398
max: 19.465255916522874
min: 5.630889362142506
sd: 3.858362757020072
RElbow_speed
mean: 7.5279665259728095
max: 12.248205125018949
min: 3.878283273187666
sd: 2.1896929375912517
LWrist_speed
skipping LWrist_speed
LWrist_vert_vel
skipping LWrist_vert_vel
LShoulder_speed
skipping LShoulder_speed

For reaching to grasp, it is not uncommon to take a speed threshold of 15cm/s as a movement of the hand (e.g., based on wrist movement translation). We should be aware however, that while such values make sense for a particular articulator, other articulators may be less mobile, and are judged to move much earlier relative to background noise micro movements. As such, we devised an 'empirical threshold' applied for the whole data, by calculating the percentile of the distributions of wrist speeds at 15cm/s, and then using that percentile to get the thresholds for the other articulators based on their observed speed distributions. The distributions were consistently long-tailed so we constructed log-transformed distributions. Supplemental figure S1, shows all the thresholds we determined with this emprical threshold procedure. (Wim)

Calculate threshold for movement as percentage¶

In [42]:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

wrist_speed = []

# get wrist speeds for all files
for file in centeredfiles:
    df = pd.read_csv(file)
    wrist_speed.extend(df['RWrist_speed'].tolist())
    wrist_speed.extend(df['LWrist_speed'].tolist())

wrist_speed = np.array(wrist_speed)

# get rid of outliers based on Tukeys rule
Q1 = np.percentile(wrist_speed, 25)
Q3 = np.percentile(wrist_speed, 75)
IQR = Q3 - Q1
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR

wrist_speed = wrist_speed[(wrist_speed > lower_bound) & (wrist_speed < upper_bound)]


# what is the max value of the speed for wrists
# speed_max = df[['RWrist_speed', 'LWrist_speed']].max().max()

speed_15cm_percentage = 15 / wrist_speed.max() * 100

# what is 15 cm/s in %
#speed_15cm_percentage = 15 / speed_max * 100

print('15 cm/s is in % ' + str(speed_15cm_percentage))

# store the thresholds for other keypoints
dynamic_thresholds = {}

# left and right should be treated as one
combined_keypoints = [
    ('LElbow_speed', 'RElbow_speed'),
    ('LShoulder_speed', 'RShoulder_speed'),
    ('LKnee_speed', 'RKnee_speed'),
    ('LHip_speed', 'RHip_speed'),
    ('LAnkle_speed', 'RAnkle_speed'),
    ('LHeel_speed', 'RHeel_speed'), 
    ('LBigToe_speed', 'RBigToe_speed'),
    ('LSmallToe_speed', 'RSmallToe_speed')
]

for keypoint_pair in combined_keypoints:
    joint_speed = []
    for file in folderstotrack:
        df = pd.read_csv(file)
        joint_speed.extend(df[keypoint_pair[0]].tolist())
        joint_speed.extend(df[keypoint_pair[1]].tolist())
    joint_speed = np.array(joint_speed)
    dynamic_threshold = (speed_15cm_percentage / 100) * joint_speed.max()
    dynamic_thresholds[keypoint_pair] = dynamic_threshold

# collect remaining keypoints
speed_columns = [col for col in df.columns if 'speed' in col and all(key not in col for key in ['Wrist', 'Elbow', 'Shoulder', 'Knee', 'Hip', 'Ankle', 'Heel', 'BigToe', 'SmallToe'])
]

for keypoint in speed_columns:
    keypoint_speed = []
    for file in folderstotrack:
        df = pd.read_csv(file)
        keypoint_speed.extend(df[keypoint].tolist())

    keypoint_speed = np.array(keypoint_speed)
    dynamic_threshold = (speed_15cm_percentage / 100) * keypoint_speed.max()
    dynamic_thresholds[keypoint] = dynamic_threshold

for key, threshold in dynamic_thresholds.items():
    print(f'{key} threshold: {threshold}')
15 cm/s is in % 14.838139997397404
('LElbow_speed', 'RElbow_speed') threshold: 29.41884905078313
('LShoulder_speed', 'RShoulder_speed') threshold: 14.464364340601986
('LKnee_speed', 'RKnee_speed') threshold: 15.45234607970934
('LHip_speed', 'RHip_speed') threshold: 9.819380891940563
('LAnkle_speed', 'RAnkle_speed') threshold: 21.405892858237596
('LHeel_speed', 'RHeel_speed') threshold: 24.101081874231696
('LBigToe_speed', 'RBigToe_speed') threshold: 19.197425758475088
('LSmallToe_speed', 'RSmallToe_speed') threshold: 20.086653993973112
Head_speed threshold: 15.587675175509778
Neck_speed threshold: 17.35276641179739
Nose_speed threshold: 15.981094566112416

Note that taking percentage is sensitive to extreme values in the distribution. Therefore, in the next step we take percentile instead

Calculate threshold for movement as percentile¶

In [17]:
# load in one file
centeredfiles = glob.glob(datafolder + '/mt_centered_*')
sample = pd.read_csv(centeredfiles[0])

# get all the keypoint columns
keypoint_columns = [col for col in sample.columns if 'speed' in col]

# for each keypoint column, prepare empty list to store later the logged values
for keypoint in keypoint_columns:
    # create a list that has name of the keypoint
    globals()[keypoint] = []

all_keypoints = {}

for file in centeredfiles:
    print('working on' + file)
    df = pd.read_csv(file)
    for keypoint in keypoint_columns:
        # log the values
        globals()[keypoint].extend(np.log(df[keypoint].tolist()))
        all_keypoints[keypoint] = globals()[keypoint]

# Define combined keypoints pairs
combined_keypoints = [
    ('LElbow_speed', 'RElbow_speed'),
    ('LShoulder_speed', 'RShoulder_speed'),
    ('LKnee_speed', 'RKnee_speed'),
    ('LHip_speed', 'RHip_speed'),
    ('LHeel_speed', 'RHeel_speed'), 
    ('LWrist_speed', 'RWrist_speed')
]

for keypoint_pair in combined_keypoints:
    left_keypoint = keypoint_pair[0]
    right_keypoint = keypoint_pair[1]

    # combine logged values
    combined_logged = np.concatenate([all_keypoints[left_keypoint], all_keypoints[right_keypoint]])

    # store the combined values in the dictionary
    all_keypoints[keypoint_pair] = combined_logged

# get percentile of 20 cm/s in RWrist_speed
# get the tuple LWrist_speed and RWrist_speed
wrist_speed = all_keypoints[('LWrist_speed', 'RWrist_speed')]
# sort the RWrist_speed
wspeed_sorted = np.sort(wrist_speed)
# get the rank of 15 cm/s
threshold = np.log(20)
rank = np.searchsorted(wspeed_sorted, threshold, side='right')
percentile = rank / len(wrist_speed) *100
print('20 cm/s is in % ' + str(percentile))
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_0_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_10_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_11_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_12_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_13_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_14_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_15_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_16_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_17_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_18_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_19_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_1_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_20_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_21_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_22_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_23_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_24_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_25_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_26_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_27_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_28_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_29_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_2_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_30_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_31_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_32_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_33_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_35_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_36_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_37_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_38_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_39_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_3_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_40_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_41_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_42_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_43_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_44_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_45_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_46_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_47_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_48_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_49_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_4_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_50_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_51_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_52_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_53_p1.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_5_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_6_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_7_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_8_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_9_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_tpose_0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_tpose_1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_0_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_100_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_101_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_102_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_103_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_104_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_105_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_106_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_107_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_108_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_109_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_10_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_110_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_111_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_112_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_113_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_11_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_12_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_13_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_14_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_15_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_16_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_17_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_18_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_19_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_1_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_20_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_21_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_22_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_23_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_24_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_25_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_26_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_27_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_28_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_29_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_2_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_30_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_31_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_32_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_33_p1.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_34_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_36_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_37_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_38_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_39_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_3_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_40_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_41_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_50_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_82_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_83_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_84_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_85_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_86_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_87_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_88_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_89_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_8_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_90_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_91_p0.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_92_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_93_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_94_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_95_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_96_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_97_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_98_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_99_p1.csv
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_9_p0.csv
20 cm/s is in % 52.351122354736376
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: divide by zero encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4136731168.py:20: RuntimeWarning: invalid value encountered in log
  globals()[keypoint].extend(np.log(df[keypoint].tolist()))
In [18]:
# store the thresholds for other keypoints
dynamic_thresholds = {}
dynamic_thresholds_log = {}


for keypoint_pair in combined_keypoints:
    # access the lists associated to the keypoint pair
    array1 = all_keypoints[keypoint_pair[0]]
    array2 = all_keypoints[keypoint_pair[1]]
    # convert into numpy array
    array1 = np.array(array1)
    array2 = np.array(array2)

    # combine the two arrays
    joint_logged = np.concatenate([array1, array2])

    # check for NaN values
    if np.isnan(joint_logged).any():
        # handle NaN values
        joint_logged = joint_logged[~np.isnan(joint_logged)]

    # calculate the percentile
    dynamic_threshold_log = np.percentile(joint_logged, percentile)
    dynamic_threshold = np.exp(dynamic_threshold_log)

    # store the threshold
    dynamic_thresholds[keypoint_pair] = dynamic_threshold
    dynamic_thresholds_log[keypoint_pair] = dynamic_threshold_log

# create list of keypoint columns that are not in combined_keypoints
speed_columns = [col for col in df.columns if 'speed' in col and all(key not in col for key in ['Wrist', 'Elbow', 'Shoulder', 'Knee', 'Hip', 'Ankle', 'Heel'])
]

# loop over the remaining keypoints lists and find the value at this percentage
for keypoint in speed_columns:
    print(keypoint)
    # convert to array
    array = np.array(globals()[keypoint])

    # Check for NaN values
    if np.isnan(array).any():
        # Handle NaN values
        array = array[~np.isnan(array)]

    # check for inf values and -inf
    if np.isinf(array).any():
        # Handle inf values
        array = array[~np.isinf(array)]

    dynamic_threshold_log = np.percentile(array, percentile)
    print(dynamic_threshold_log)
    dynamic_threshold = np.exp(dynamic_threshold_log)
    print(dynamic_threshold)
    dynamic_thresholds[keypoint] = dynamic_threshold
    dynamic_thresholds_log[keypoint] = dynamic_threshold_log

for key, threshold in dynamic_thresholds.items():
    print(f'{key} threshold: {threshold}')

for key, threshold in dynamic_thresholds_log.items():
    print(f'{key} threshold: {threshold}')
Head_speed
2.0238553748054136
7.567444098264241
('LElbow_speed', 'RElbow_speed') threshold: 11.277574378580344
('LShoulder_speed', 'RShoulder_speed') threshold: 8.550446304012498
('LKnee_speed', 'RKnee_speed') threshold: 6.535496954264708
('LHip_speed', 'RHip_speed') threshold: 7.408189975161015
('LHeel_speed', 'RHeel_speed') threshold: 3.6409926065783074
('LWrist_speed', 'RWrist_speed') threshold: 19.970355651487136
Head_speed threshold: 7.567444098264241
('LElbow_speed', 'RElbow_speed') threshold: 2.422816185586166
('LShoulder_speed', 'RShoulder_speed') threshold: 2.14598348088604
('LKnee_speed', 'RKnee_speed') threshold: 1.8772483892139382
('LHip_speed', 'RHip_speed') threshold: 2.0025861416102306
('LHeel_speed', 'RHeel_speed') threshold: 1.2922563385892867
('LWrist_speed', 'RWrist_speed') threshold: 2.9942489565574295
Head_speed threshold: 2.0238553748054136

Plot the thresholds for movement¶

In [19]:
import matplotlib.pyplot as plt

# Define the number of subplots (one for each keypoint)
num_keypoints = len(dynamic_thresholds)
num_cols = 2  # Number of columns in the subplot grid
num_rows = (num_keypoints + num_cols - 1) // num_cols  # Number of rows needed

fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, 5 * num_rows))

# Flatten the axes array for easy iteration if it is multidimensional
if num_rows > 1:
    axes = axes.flatten()

# Loop over keys in dynamic_thresholds
for i, keypoint in enumerate(dynamic_thresholds.keys()):
    logged_values = all_keypoints[keypoint]
    print(keypoint)
    print(logged_values)

    # if there any inf values skip them
    if np.isinf(logged_values).any():
        # Handle inf values
        logged_values = logged_values[~np.isinf(logged_values)]
    
    # Plot the histogram of the logged values
    axes[i].hist(logged_values, bins=100, density=True)
    axes[i].set_title(keypoint)
    
    # Determine which dynamic threshold to use based on keypoint type
    if isinstance(keypoint, tuple):
        # Combined keypoints
        threshold_log = dynamic_thresholds_log[keypoint]
        threshold = dynamic_thresholds[keypoint]
    else:
        # Individual keypoints
        threshold_log = dynamic_thresholds_log[keypoint]
        threshold = dynamic_thresholds[keypoint]
    
    # Add a vertical line for the threshold (logged value)
    axes[i].axvline(threshold_log, color='r')
    axes[i].text(threshold_log, 0.20, f'{threshold_log:.2f}', rotation=0, va='bottom', ha='center')  # Label in log
    
    # Calculate the corresponding unlogged threshold value
    threshold_unlog = np.exp(threshold_log)
    axes[i].text(np.log(threshold_unlog), 0.15, f'{threshold_unlog:.2f}', rotation=0, va='center', ha='center')  # Label in unlog
    
    # Get the current axis
    ax = axes[i]
    
    # Get the current tick positions on the x-axis
    x_ticks = ax.get_xticks()
    
    # Compute the corresponding unlogged values
    unlogged_values = np.exp(x_ticks)
    
    # Format the tick labels to include both logged and unlogged values
    x_labels = [f'{log_val:.2f} ({unlog_val:.2f})' for log_val, unlog_val in zip(x_ticks, unlogged_values)]
    
    # Set the new tick labels
    ax.set_xticklabels(x_labels)
    # Rotate x labels
    ax.tick_params(axis='x', rotation=45)
    
    # Set the labels for the x-axis
    ax.set_xlabel('Logged values (Unlogged values)')

# Remove any unused subplots
for j in range(i + 1, len(axes)):
    fig.delaxes(axes[j])

plt.tight_layout()
plt.show()
('LElbow_speed', 'RElbow_speed')
[3.01940643 3.01940643 3.00330792 ... 1.83128637 1.84566428 1.85983839]
('LShoulder_speed', 'RShoulder_speed')
[2.28757072 2.28757072 2.3131217  ... 1.28922951 1.27587209 1.26233382]
('LKnee_speed', 'RKnee_speed')
[2.06147757 2.06147757 2.04878219 ...        nan        nan        nan]
('LHip_speed', 'RHip_speed')
[       -inf        -inf  3.29334545 ...  0.47488421  0.24305145
 -0.05928257]
('LHeel_speed', 'RHeel_speed')
[0.99208283 0.99208283 0.99668797 ... 0.95888441 0.95888441 0.95888441]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4040276043.py:60: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
  ax.set_xticklabels(x_labels)
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4040276043.py:60: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
  ax.set_xticklabels(x_labels)
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4040276043.py:60: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
  ax.set_xticklabels(x_labels)
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4040276043.py:60: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
  ax.set_xticklabels(x_labels)
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4040276043.py:60: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
  ax.set_xticklabels(x_labels)
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4040276043.py:60: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
  ax.set_xticklabels(x_labels)
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\4040276043.py:60: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
  ax.set_xticklabels(x_labels)
('LWrist_speed', 'RWrist_speed')
[ 4.00823891  4.00823891  4.00164051 ...  0.67081348  0.23961315
 -0.53496938]
Head_speed
[1.196833773044522, 1.196833773044522, 1.2070578900856193, 1.2171785316109778, 1.2271977711477753, 1.2371176205144103, 1.2469400322450954, 1.2566669018965326, 1.2663000702434764, 1.3060439413801088, 1.2659056223189247, 1.2338757073112363, 1.231105598975322, 1.2372600051616045, 1.2325049134760913, 1.212665719810023, 1.185920058830645, 1.1326883078309185, 1.1492554386294482, 1.1985725886924878, 1.2420101555062597, 1.2799905582689686, 1.3260527567662448, 1.3729190109585945, 1.396692188584597, 1.3906823798476238, 1.3788250532816528, 1.36134661566332, 1.3239305498520684, 1.2852378545164005, 1.2744796369639553, 1.2694532781432235, 1.2372112527914318, 1.1812065778742273, 1.1062379570920189, 1.0065344255039594, 0.9115998753863441, 0.8513720027909596, 0.8132094759426741, 0.8046763533237159, 0.8621090048609782, 0.920162927042241, 0.9442582409858183, 0.978810099106977, 1.030956354190974, 1.06878461683602, 1.1124508713956442, 1.1828598192398792, 1.2274205541384693, 1.2058447526217255, 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No description has been provided for this image

Annotate movement based on thresholds¶

Note that y and z dimensions are swapped (such that z is vertical)

In [20]:
def determine_movement(value, threshold):
    if value > threshold:
        return 1
    else:
        return 0
In [130]:
wnoise
Out[130]:
{'Wrist_speed': [8.148664198131078,
  11.740306021401924,
  4.618764556306746,
  1.9990770440761167],
 'Wrist_vert_vel': [0.3149308813912007,
  3.614311484827629,
  -5.6034372531266925,
  2.0154133461692703],
 'Shoulder_speed': [10.854913809687398,
  19.465255916522874,
  5.630889362142506,
  3.858362757020072]}
In [21]:
annofolder = curfolder + '/MT_annotated'

Annotate events¶

In [298]:
noise
Out[298]:
{'Heel_speed': [5.004875624374936,
  16.954267438838876,
  0.5106655303723385,
  2.7607815463614553],
 'Hip_speed': [10.326424849596721,
  54.49077758547286,
  0.9239982664543706,
  9.017960876402839],
 'Head_speed': [4.3243746575386215,
  18.32696145797376,
  -0.7498397719774741,
  2.719665513018643],
 'Knee_speed': [8.812762536028025,
  33.385013262411306,
  2.4257931690567514,
  4.36462620292907]}
In [299]:
import pandas as pd
import numpy as np

for file in centeredfiles:
    print('working on' + file)
    # last element is trialid
    trialid = file.split('\\')[-1]
    trialid = trialid.split('.')[0]

    # load it
    mt = pd.read_csv(file)

    # get the sampling rate
    sr = 1 / (mt['Time'].diff().mean())

    # show columns
    cols = mt.columns

    # put away 'Time' column from the list
    cols = [x for x in cols if x != 'Time']
    cols = [x for x in cols if x != 'TrialID']
    vvcols = [x for x in cols if 'vert_vel' in x]

    # keep only those with speed
    speedcols = [x for x in cols if 'speed' in x]

    # for each unique colname (cols), get the threshold for movement
    for col in speedcols:
        # annotate movement
        mt[col + '_movement'] = None
        # if the col starts with R or L, then we will find the threshold in a tuple
        if col.startswith('R'):
            threshold = dynamic_thresholds[(col.replace('R', 'L'), col)]
            # lets treat keypoints differently, as they are differently affected by noise, but mostly, we add some noise (+ sd) to the calculated dynamic threshold
            if 'Wrist' in col:
                threshold = threshold + wnoise[col.replace('R', '')][0] + wnoise[col.replace('R', '')][3]*2 # threshold + mean noise + sd noise
            elif 'Elbow' in col:
                threshold = threshold + wnoise[col.replace('R', '')][0] + wnoise[col.replace('R', '')][3]*2
                print(threshold)
            elif 'Knee' in col:
                threshold = threshold + noise[col.replace('R', '')][0] #- noise[col.replace('R', '')][3]    # here we add only mean noise as it seems that sd would smooth too much meaningful movement away
            elif 'Shoulder' in col:
                threshold = threshold + wnoise[col.replace('R', '')][0] - wnoise[col.replace('R', '')][3]
            elif 'Hip' in col: 
                threshold = threshold + noise[col.replace('R', '')][0] + noise[col.replace('R', '')][3]*2
            elif 'Heel' in col:
                threshold = threshold + noise[col.replace('R', '')][0] + noise[col.replace('R', '')][3]*2
            else:
                if 'Elbow' in col: # we ignore Elbow because we are fine with wrist to annotate arm movement
                    continue
                else:
                    threshold = threshold + noise[col.replace('R', '')][0] + noise[col.replace('R', '')][3]
     
        elif col.startswith('L'):
            threshold = dynamic_thresholds[(col, col.replace('L', 'R'))]

            if 'Wrist' in col:
                threshold = threshold + wnoise[col.replace('L', '')][0] + wnoise[col.replace('L', '')][3]*2
            elif 'Elbow' in col:
                threshold = threshold + wnoise[col.replace('L', '')][0] + wnoise[col.replace('L', '')][3]*2
            elif 'Knee' in col:
                threshold = threshold + noise[col.replace('L', '')][0] #- noise[col.replace('L', '')][3]
            elif 'Shoulder' in col:
                threshold = threshold + wnoise[col.replace('L', '')][0] - wnoise[col.replace('L', '')][3]
            elif 'Hip' in col:
                threshold = threshold + noise[col.replace('L', '')][0] + noise[col.replace('L', '')][3]*2
            elif 'Heel' in col:
                threshold = threshold + noise[col.replace('L', '')][0] + noise[col.replace('L', '')][3]*2
            else:
                threshold = threshold + noise[col.replace('L', '')][0] + noise[col.replace('L', '')][3]
            
        else:
            threshold = dynamic_thresholds[col]
            if 'Head' in col:
                threshold = threshold + noise[col][0] + noise[col][3]*2 
            else:
                threshold = threshold + noise[col][0] + noise[col][3]

        # determine movement
        mt[col + '_movement'] = mt[col].apply(lambda x: determine_movement(x, threshold))

        # annotate events
        mt[col + '_event'] = (mt[col + '_movement'] != mt[col + '_movement'].shift(1)) & (mt[col + '_movement'] == 1)
        mt[col + '_event'] = mt[col + '_event'].cumsum()
        # if there is no movement, then event is 0
        mt.loc[mt[col + '_movement'] == 0, col + '_event'] = 0 

    for col in vvcols:
        # for each of these columns, create a col_movement column
        mt[col + '_movement'] = None
        # threshold
        if col.startswith('R'):
            vvel_threshold = 20 + wnoise[col.replace('R', '')][0] + wnoise[col.replace('R', '')][3] # in the velocity, the noise is not so high
        elif col.startswith('L'):
            vvel_threshold = 20 + wnoise[col.replace('L', '')][0] + wnoise[col.replace('L', '')][3]
        # if the speed in col_speed is smaller than -15 or bigger than 15, then movement is 1, else 0
        mt.loc[(mt[col] >= vvel_threshold) | (mt[col] <= -vvel_threshold), col + '_movement'] = 1
        mt.loc[(mt[col] < vvel_threshold) & (mt[col] > -vvel_threshold), col + '_movement'] = 0
        # annotate events
        mt[col + '_movement_event'] = (mt[col + '_movement'] != mt[col + '_movement'].shift(1)) & (mt[col + '_movement'] == 1)
        mt[col + '_movement_event'] = mt[col + '_movement_event'].cumsum()
        # if there is no movement, then event is 0
        mt.loc[mt[col + '_movement'] == 0, col + '_movement_event'] = 0

    # get rid of all columns with _y, _x, _z
    mt = mt[[x for x in mt.columns if not any(y in x for y in ['_x', '_y', '_z'])]]
            
    # save the annotated file
    mt.to_csv(annofolder + '/' + trialid + '_annotated.csv', index=False)
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_8_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_9_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_tpose_0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_1_tpose_1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_0_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_100_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_101_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_102_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_103_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_107_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_108_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_109_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_10_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_110_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_112_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_11_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_12_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_13_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_14_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_15_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_16_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_17_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_18_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_19_p1.csv
23.184926779735658
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_1_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_20_p1.csv
23.184926779735658
working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_21_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_22_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_23_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_24_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_25_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_26_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_27_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_28_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_29_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_2_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_30_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_31_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_32_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_33_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_34_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_35_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_36_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_37_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_38_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_39_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_3_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_40_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_41_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_43_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_44_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_45_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_46_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_48_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_49_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_4_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_50_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_51_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_5_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_60_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_61_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_62_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_64_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_65_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_67_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_68_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_69_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_6_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_70_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_89_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_8_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_90_p0.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_92_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_94_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_95_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_96_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_97_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_98_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_99_p1.csv
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working one:\FLESH_ContinuousBodilyEffort\TS_processing/TS_motiontracking\mt_centered_0_2_9_p0.csv
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In [208]:
annofiles = glob.glob(annofolder + '/*.csv')
# skip those files that have ELAN in the name
annofiles = [x for x in annofiles if 'ELAN' not in x]
# keep only centered in
annofiles = [x for x in annofiles if 'centered' in x]
print(annofiles)
['e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_0_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_10_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_11_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_12_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_13_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_14_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_15_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_16_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_17_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_18_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_19_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_1_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_20_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_21_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_22_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_23_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_24_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_25_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_26_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_27_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_28_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_29_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_2_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_30_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_31_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_32_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_33_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_35_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_36_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_37_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_38_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_39_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_3_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_40_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_41_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_42_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_43_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_44_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_45_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_46_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_47_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_48_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_49_p1_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_4_p0_annotated.csv', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\mt_centered_0_1_50_p1_annotated.csv', 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Functions needed for annotation¶

In [324]:
## function to parse the events into chunks

# Function to get chunks from a column
def get_chunks(df, time_col, event_col):
    chunks = []
    current_chunk = None

    for idx, row in df.iterrows():
        if current_chunk is None:
            current_chunk = {'value': row[event_col], 'start_idx': idx, 'start_time': row[time_col]}
        elif row[event_col] != current_chunk['value']:
            current_chunk['end_idx'] = idx - 1
            current_chunk['end_time'] = df.loc[idx - 1, time_col]
            chunks.append(current_chunk)
            current_chunk = {'value': row[event_col], 'start_idx': idx, 'start_time': row[time_col]}
        if idx == len(df) - 1:
            current_chunk['end_idx'] = idx
            current_chunk['end_time'] = row[time_col]
            chunks.append(current_chunk)

    chunk_data = []
    for chunk in chunks:
        duration = chunk['end_time'] - chunk['start_time']
        chunk_data.append([chunk['value'], chunk['start_idx'], chunk['end_idx'], chunk['start_time'], chunk['end_time'], duration])

    chunks_df = pd.DataFrame(chunk_data, columns=['value', 'start_idx', 'end_idx', 'start_time', 'end_time', 'duration'])
    return chunks_df


## detect and merge short pauses

def detect_and_merge_short_pauses(chunk_df, col, df):
    short_pauses = []
    # Print rows that have value 0 and duration less than 250
    if 'Wrist' in col or 'Elbow' in col:
        pause_threshold = 250
    elif 'Shoulder' in col:
        pause_threshold = 450
    elif 'Knee' in col:
        pause_threshold = 300
    # elif 'Head' in col:
    #     pause_threshold = 500
    else:
        pause_threshold = 350

    short_pause_rows = chunk_df[(chunk_df['value'] == 0) & (chunk_df['duration'] < pause_threshold)]
    
    # if short_pause_rows is not empty, loop over
    if not short_pause_rows.empty:
        # if these rows do not contain first and last row, append them to short_pauses
        for idx, row in short_pause_rows.iterrows():
            # if start_idx is not 0 and end_idx is not the last index, append to short_pauses
            if row['start_idx'] != 0 and row['end_idx'] != len(df) - 1:
                short_pauses.append(row)
    
        # If short pauses is empty, we do not need to merge anything
        if not short_pauses:
            print('We do not need to merge')
            df = df
            return df
        
        print('We need to merge')  
        # Check what is the value of the previous row and the next row in chunk
        for pause in short_pauses:
            # Get the index of the row in df
            idx = pause.name      
            # Get the index of the previous row
            prev_idx = idx - 1
            # Get the start index of the previous row
            new_start_index = chunk_df.loc[prev_idx, 'start_idx']
            # Value of the previous row
            prev_value = chunk_df.loc[prev_idx, 'value']
            # Get the index of the next row
            next_idx = idx + 1
            # Get the end index of the next row
            new_end_idx = chunk_df.loc[next_idx, 'end_idx']
            # Everything from new_start_index to new_end_idx should be merged and named as previous value
            # In the original dataframe, set the value to the previous value in this range
            df.loc[new_start_index:new_end_idx, col] = prev_value

    return df


## merge nonzero segments
def merge_nonzero_segments(chunks):
    merged_all = []

    for df in chunks:
        # Filter rows with non-zero values
        non_zero_rows = df[df['value'] != 0].reset_index(drop=True)
        
        #print(non_zero_rows)
        # Initialize list to store merged segments
        merged_segments = []

        # If the non_zero rows are empty, we do not need to do anything
        if non_zero_rows.empty:
            print('No non-zero rows')
            continue
        else:
            # Initialize variables to track current segment
            current_segment = non_zero_rows.iloc[0].copy()

            # Iterate through non-zero rows to merge consecutive segments
            for i in range(1, len(non_zero_rows)):
                row = non_zero_rows.iloc[i]
            
                if current_segment['end_idx'] + 1 == row['start_idx']:
                    # Merge segments
                    current_segment['end_idx'] = row['end_idx']
                    current_segment['duration'] += row['duration']
                else:
                    # Append current segment to merged segments list
                    merged_segments.append(current_segment)
                    # Start new segment
                    current_segment = row.copy()

            # Append the last segment
            merged_segments.append(current_segment)

            # Convert list of merged segments to DataFrame
            merged_df = pd.DataFrame(merged_segments)

            # Append to merged_all
            merged_all.append(merged_df)

    return merged_all

## process fake events

def process_fake_events(chunk, df_all):
    #for chunk in chunks:
    col = chunk['column'][0]
    df = chunk

    ## FAKE EVENTS
    nomov = []  
    if 'Wrist' in col:
        fake_threshold = 400
    if 'Elbow' in col:
        fake_threshold = 350
    elif 'Shoulder' in col:
        fake_threshold = 350
    elif 'Knee' in col:
        fake_threshold = 300
    elif 'Head' in col:
        fake_threshold = 150
    else:
        fake_threshold = 400
    # Print rows that don't have value 0 and duration less than x
    fake_event_rows = df[(df['value'] != 0) & (df['duration'] < fake_threshold)]  
    print(fake_event_rows)

    # Add those rows to nomov
    for idx, row in fake_event_rows.iterrows():
        nomov.append(row)
    
    # If nomov is empty, we do not need to do anything
    if not nomov:
        print('No fake events found')
        df_all = df_all
    else:
        # These fake events need to be turned into 0s in the original dataframe
        print('We need to turn fake events into 0s')
        # For each row in nomov, set the value in the original dataframe to 0
        for fake_event in nomov:
            start = int(fake_event['start_idx'])
            end = int(fake_event['end_idx'])
            # In the original dataframe, set the value to 0 in this range
            df_all.loc[start:end, col] = 0

    return df_all


# Function to check if there is any overlap
def check_overlap(df, non_zero_df, zero_df, wrist_name):
    first_non_zero = non_zero_df.iloc[0]
    last_non_zero = non_zero_df.iloc[-1]

    startstodel = []
    endstodel = []
    
    for zero_row in zero_df.itertuples(index=False):
        # Check if the first non-zero chunk is completely within the zero chunk
        if first_non_zero.start_idx >= zero_row.start_idx and first_non_zero.end_idx <= zero_row.end_idx:
            print(f"First non-zero chunk {first_non_zero.start_idx}-{first_non_zero.end_idx} in {first_non_zero.column} is completely within zero chunk {zero_row.start_idx}-{zero_row.end_idx} in {wrist_name}_vert_vel_movement_event")
            # attach the start and end index of this chunk, but only if the nonzero chunk is smaller than 100
            if (first_non_zero.end_idx - first_non_zero.start_idx) <= 50:
                startstodel.append(first_non_zero.start_idx)
                endstodel.append(first_non_zero.end_idx)
        
        # check if there is partial overlap
        elif first_non_zero.start_idx > zero_row.start_idx and first_non_zero.end_idx > zero_row.start_idx and first_non_zero.end_idx > zero_row.end_idx and first_non_zero.start_idx < zero_row.end_idx:
            print(f"First non-zero chunk {first_non_zero.start_idx}-{first_non_zero.end_idx} in {first_non_zero.column} partially overlaps with zero chunk {zero_row.start_idx}-{zero_row.end_idx} in {wrist_name}_vert_vel_movement_event")
            # attach the start and end index of this chunk
            if (zero_row.end_idx - first_non_zero.start_idx) <= 50:
                startstodel.append(first_non_zero.start_idx)
                endstodel.append(zero_row.end_idx)
        
        else:
            print('no overlap')
        
        # Check if the last non-zero chunk is completely within the zero chunk
        if last_non_zero.start_idx >= zero_row.start_idx and last_non_zero.end_idx <= zero_row.end_idx:
            print(f"Last non-zero chunk {last_non_zero.start_idx}-{last_non_zero.end_idx} in {last_non_zero.column} is completely within zero chunk {zero_row.start_idx}-{zero_row.end_idx} in {wrist_name}_vert_vel_movement_event")
            # attach
            if (last_non_zero.end_idx - last_non_zero.start_idx) <= 50:
            # check if the last non-zero chunk is within the last 1/4 of idices
                if last_non_zero.start_idx >= (len(df) - len(df)/4):
                    startstodel.append(last_non_zero.start_idx)
                    endstodel.append(last_non_zero.end_idx)
            
        # check if there is partial overlap
        elif last_non_zero.start_idx < zero_row.start_idx and last_non_zero.end_idx > zero_row.start_idx and last_non_zero.end_idx <= zero_row.end_idx:
            print(f"Last non-zero chunk {last_non_zero.start_idx}-{last_non_zero.end_idx} in {last_non_zero.column} partially overlaps with zero chunk {zero_row.start_idx}-{zero_row.end_idx} in {wrist_name}_vert_vel_movement_event")
            # attach
            if (last_non_zero.end_idx - zero_row.start_idx) <= 50:
                if last_non_zero.start_idx >= (len(df) - len(df)/4):
                    startstodel.append(zero_row.start_idx)
                    endstodel.append(last_non_zero.end_idx)

        else:
            print('no overlap')

    return startstodel, endstodel

Loop over files and annotate events¶

In [325]:
sample = pd.read_csv(annofiles[0])
eventcols = [x for x in sample.columns if 'event' in x]
eventcols = [x for x in eventcols if 'Wrist' not in x]

for file in annofiles:
    print('working on ' + file)
    ### file
    file_df = pd.read_csv(file)
    trialid = file_df['TrialID'][0]

    ##### BODY EXCEPT WRIST #####   

    #### merging short pauses
    # Initialize variables
    chunks = []
    current_chunk = None

    # Dictionary to store results for each column
    chunk_results = {}

    # Loop through each event column and apply the function
    for col in eventcols:
        chunks_df = get_chunks(file_df, 'Time', col)
        chunk_results[col] = chunks_df

    # turn the dictionary into a list, also with info about column
    chunks = []
    for col, df in chunk_results.items():
        df['column'] = col
        chunks.append(df)

    # merge short pauses
    for chunk in chunks:
        col = chunk['column'][0]
        df = chunk
        file_df1 = detect_and_merge_short_pauses(df, col, file_df)

    #### merging non-zero segments
    # apply the chunk function again on the new dataframe
    chunk_results_new = {}

    # Loop through each event column and apply the function
    for col in eventcols:
        chunks_df = get_chunks(file_df1, 'Time', col)
        chunk_results_new[col] = chunks_df

    # turn the dictionary into a list, also with info about column
    chunks_new = []
    for col, df in chunk_results_new.items():
        df['column'] = col
        chunks_new.append(df)

    # merge nonzero segments
    merged_all = merge_nonzero_segments(chunks_new)

    for chunk in merged_all:
        for _, row in chunk.iterrows():
            start = row['start_idx']
            end = row['end_idx']
            value = row['value']
            column = row['column']

            # Update the corresponding column in file_df
            file_df1.loc[start:end, column] = value

    ### process fake events
    # perform the chunk function again
    chunk_results_final = {}

    for col in eventcols:
        chunks_df = get_chunks(file_df1, 'Time', col)
        chunk_results_final[col] = chunks_df

    # convert dict into list
    chunks_final = []

    for col, df in chunk_results_final.items():
        df['column'] = col
        chunks_final.append(df)

    # get rid of fake events
    for chunk in chunks_final:
        # process fake events
        file_df2 = process_fake_events(chunk, file_df1)
    
    #### WRIST ####
    # lets do chunks again, but only on wrist event cols
    wristcols = [x for x in file_df2.columns if 'Wrist' in x]
    wristcols = [x for x in wristcols if 'event' in x]

    #### first only vert_vel
    vvcols = [x for x in wristcols if 'vert_vel' in x]

    ### merging short pauses
    # chunks
    vvchunks_results = {}
    for col in vvcols:
        vvchunks_df = get_chunks(file_df2, 'Time', col)
        vvchunks_results[col] = vvchunks_df

    # convert dict into list
    vvchunks = []
    for col, df in vvchunks_results.items():
        df['column'] = col
        vvchunks.append(df)

    # merge short pauses
    for chunk in vvchunks:
        col = chunk['column'][0]
        df = chunk
        file_df3 = detect_and_merge_short_pauses(df, col, file_df2)

    ### merging non-zero segments
    # apply the chunk function again on the new dataframe
    vvchunk_results_new = {}
    for col in vvcols:
        vvchunks_df = get_chunks(file_df3, 'Time', col)
        vvchunk_results_new[col] = vvchunks_df

    # convert dict into list
    vvchunks_new = []
    for col, df in vvchunk_results_new.items():
        df['column'] = col
        vvchunks_new.append(df)

    # merge events
    vvmerged_all = merge_nonzero_segments(vvchunks_new)

    for chunk in vvmerged_all:
        for _, row in chunk.iterrows():
            start = row['start_idx']
            end = row['end_idx']
            value = row['value']
            column = row['column']

            # Update the corresponding column in file_df
            file_df3.loc[start:end, column] = value

    ### process fake events
    # perform the chunk function again
    vvchunk_results_final = {}
    for col in vvcols:
        vvchunks_df = get_chunks(file_df3, 'Time', col)
        vvchunk_results_final[col] = vvchunks_df

    # convert dict into list
    vvchunks_final = []
    for col, df in vvchunk_results_final.items():
        df['column'] = col
        vvchunks_final.append(df)

    for chunk in vvchunks_final:
        # delete fake events
        file_df4 = process_fake_events(chunk, file_df3)

    # now we have to check for overlap
    # now the chunk function
    wchunk_results = {}
    for col in wristcols:
        wchunks_df = get_chunks(file_df4, 'Time', col)
        wchunk_results[col] = wchunks_df

    # convert dict into list
    wchunks = []
    for col, df in wchunk_results.items():
        df['column'] = col
        wchunks.append(df)

    # find the chunk in wchunks_results_final_list tHAT HAS column value RWrist_speed_event
    rightspeed = next((df for df in wchunks if 'RWrist_speed_event' in df['column'].values), None)
    leftspeed = next((df for df in wchunks if 'LWrist_speed_event' in df['column'].values), None)
    rightvel = next((df for df in wchunks if 'RWrist_vert_vel_movement_event' in df['column'].values), None)
    leftvel = next((df for df in wchunks if 'LWrist_vert_vel_movement_event' in df['column'].values), None)

    lefttocheck = leftspeed[leftspeed['value'] != 0]
    righttocheck = rightspeed[rightspeed['value'] != 0]

    leftzero = leftvel[leftvel['value'] == 0]
    rightzero = rightvel[rightvel['value'] == 0]

    # if lefttocheck is not empty, check for overlaps
    if not lefttocheck.empty:
        print("Checking LWrist_event vs LWrist_vv_event")
        sindex1, eindex1 = check_overlap(file_df4, lefttocheck, leftzero, "LWrist")
        # replace the values in the original dataframe with 0
        for start, end in zip(sindex1, eindex1):
            file_df4.loc[start:end, 'LWrist_speed_event'] = 0

    # if righttocheck is not empty, check for overlaps
    if not righttocheck.empty:
        print("\nChecking RWrist_event vs RWrist_vv_event")
        sindex2, eindex2 =check_overlap(file_df4, righttocheck, rightzero, "RWrist")
        # replace the values in the original dataframe with 0
        for start, end in zip(sindex2, eindex2):
            # within this indices, replace the values with 0
            file_df4.loc[start:end, 'RWrist_speed_event'] = 0

    ### merging short pauses 
    # now we work with speed only
    wristcols = [x for x in wristcols if 'speed' in x]   
    # merge short pauses
    
    #  get chunks
    wchunk_results_new = {}
    for col in wristcols:
        wchunks_df = get_chunks(file_df4, 'Time', col)
        wchunk_results_new[col] = wchunks_df

    # convert dict into list
    wchunks_new = []
    for col, df in wchunk_results_new.items():
        df['column'] = col
        wchunks_new.append(df)

    for chunk in wchunks_new:
        col = chunk['column'][0]
        df = chunk
        # detect and merge short pauses
        file_df5 = detect_and_merge_short_pauses(df, col, file_df4)

    ### merging non-zero segments
    # apply the chunk function again on the new dataframe
    wchunk_results_new2 = {}
    for col in wristcols:
        wchunks_df = get_chunks(file_df5, 'Time', col)
        wchunk_results_new2[col] = wchunks_df

    # convert dict into list
    wchunks_new2 = []
    for col, df in wchunk_results_new2.items():
        df['column'] = col
        wchunks_new2.append(df)

    # merge events
    wmerged_all = merge_nonzero_segments(wchunks_new2)

    for chunk in wmerged_all:
        for _, row in chunk.iterrows():
            #print(row)
            start = row['start_idx']
            end = row['end_idx']
            value = row['value']
            column = row['column']

            # Update the corresponding column in file_df
            file_df5.loc[start:end, column] = value
        
    ### process fake events
    # perform the chunk function again
    wchunk_results_final = {}
    for col in wristcols:
        wchunks_df = get_chunks(file_df5, 'Time', col)
        wchunk_results_final[col] = wchunks_df

    # convert dict into list
    wchunks_final = []
    for col, df in wchunk_results_final.items():
        df['column'] = col
        wchunks_final.append(df)

    # get rid of fake events
    for chunk in wchunks_final:
        file_df6 = process_fake_events(chunk, file_df5)

    ### SAVING

    # make copy of file_df that has only 'event' columns in
    df_final = file_df6.copy()
    # drop other columns
    df_final = df_final[[x for x in df_final.columns if 'event' in x or 'Time' in x or 'TrialID' in x]]

    #for each column, everywhere where is 0, put 'nomovement', elsewhere 'movement'
    for col in df_final.columns:
        if 'event' in col:
            df_final[col] = np.where(df_final[col] == 0, 'nomovement', 'movement')

    # save the annotated file
    df_final.to_csv(annofolder + '/' + trialid + '_ELAN_anno.csv', index=False)


    
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_0_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time   duration  \
1      1        102      106      1700.0  1766.666667  66.666667   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
0      1          0       14     0.000000   233.333333  233.333333   
2      2         93      103  1550.000000  1716.666667  166.666667   
4      3        224      235  3733.333333  3916.666667  183.333333   

               column  
0  RElbow_speed_event  
2  RElbow_speed_event  
4  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 0-15 in LWrist_speed_event is completely within zero chunk 0-299 in LWrist_vert_vel_movement_event
Last non-zero chunk 221-237 in LWrist_speed_event is completely within zero chunk 0-299 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 0-14 in RWrist_speed_event is completely within zero chunk 0-299 in RWrist_vert_vel_movement_event
Last non-zero chunk 218-238 in RWrist_speed_event is completely within zero chunk 0-299 in RWrist_vert_vel_movement_event
   value  start_idx  end_idx   start_time     end_time    duration  \
3      3        218      238  3633.333333  3966.666667  333.333333   

               column  
3  RWrist_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
3      3        221      237  3683.333333    3950.0  266.666667   

               column  
3  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_10_p1_annotated.csv
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        287      301  4783.333333  5016.666667  233.333333   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        141      144  2350.000000  2400.000000      50.0   
3      2        292      298  4866.666667  4966.666667     100.0   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        180      189      3000.0  3150.000000  150.000000   
3      2        294      298      4900.0  4966.666667   66.666667   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
We need to turn fake events into 0s
No non-zero rows
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        285      299      4750.0  4983.333333  233.333333   

                           column  
1  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 284-306 in LWrist_speed_event is completely within zero chunk 0-318 in LWrist_vert_vel_movement_event
Last non-zero chunk 284-306 in LWrist_speed_event is completely within zero chunk 0-318 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 283-309 in RWrist_speed_event is completely within zero chunk 0-318 in RWrist_vert_vel_movement_event
Last non-zero chunk 283-309 in RWrist_speed_event is completely within zero chunk 0-318 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_11_p1_annotated.csv
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        163      179  2716.666667  2983.333333  266.666667   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        163      176  2716.666667  2933.333333  216.666667   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 158-185 in LWrist_speed_event is completely within zero chunk 0-187 in LWrist_vert_vel_movement_event
Last non-zero chunk 158-185 in LWrist_speed_event is completely within zero chunk 0-187 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 159-187 in RWrist_speed_event is completely within zero chunk 0-187 in RWrist_vert_vel_movement_event
Last non-zero chunk 159-187 in RWrist_speed_event is completely within zero chunk 0-187 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_12_p1_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        211      218  3516.666667  3633.333333  116.666667   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time   duration  \
1      1        213      218      3550.0  3633.333333  83.333333   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
No non-zero rows
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        210      218      3500.0  3633.333333  133.333333   

                           column  
1  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 203-218 in LWrist_speed_event is completely within zero chunk 0-218 in LWrist_vert_vel_movement_event
Last non-zero chunk 203-218 in LWrist_speed_event is completely within zero chunk 0-218 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 204-218 in RWrist_speed_event is completely within zero chunk 0-218 in RWrist_vert_vel_movement_event
Last non-zero chunk 204-218 in RWrist_speed_event is completely within zero chunk 0-218 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_13_p1_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        195      202      3250.0  3366.666667  116.666667   

                           column  
1  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 187-202 in LWrist_speed_event is completely within zero chunk 0-202 in LWrist_vert_vel_movement_event
Last non-zero chunk 187-202 in LWrist_speed_event is completely within zero chunk 0-202 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 187-202 in RWrist_speed_event is completely within zero chunk 0-202 in RWrist_vert_vel_movement_event
Last non-zero chunk 187-202 in RWrist_speed_event is completely within zero chunk 0-202 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_14_p1_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         77       88  1283.333333  1466.666667  183.333333   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1         79       87  1316.666667    1450.0  133.333333   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         81       92      1350.0  1533.333333  183.333333   
3      2        276      276      4600.0  4600.000000    0.000000   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1        273      276      4550.0    4600.0      50.0   

                           column  
1  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 67-92 in LWrist_speed_event is completely within zero chunk 0-276 in LWrist_vert_vel_movement_event
Last non-zero chunk 267-276 in LWrist_speed_event is completely within zero chunk 0-276 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 68-97 in RWrist_speed_event is completely within zero chunk 0-276 in RWrist_vert_vel_movement_event
Last non-zero chunk 258-276 in RWrist_speed_event is completely within zero chunk 0-276 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_15_p1_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1         80       85  1333.333333  1416.666667  83.333333   

             column  
1  Head_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1        238      239  3966.666667  3983.333333  16.666667   

                           column  
1  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 224-239 in LWrist_speed_event is completely within zero chunk 0-239 in LWrist_vert_vel_movement_event
Last non-zero chunk 224-239 in LWrist_speed_event is completely within zero chunk 0-239 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 226-239 in RWrist_speed_event is completely within zero chunk 0-239 in RWrist_vert_vel_movement_event
Last non-zero chunk 226-239 in RWrist_speed_event is completely within zero chunk 0-239 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_16_p1_annotated.csv
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time   duration  \
1      1        164      165  2733.333333    2750.0  16.666667   

             column  
1  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         54       64   900.000000  1066.666667  166.666667   
3      2        185      198  3083.333333  3300.000000  216.666667   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         55       63   916.666667  1050.000000  133.333333   
3      2        188      197  3133.333333  3283.333333  150.000000   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
We need to turn fake events into 0s
We do not need to merge
No non-zero rows
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         58       71   966.666667  1183.333333  216.666667   
3      2        190      205  3166.666667  3416.666667  250.000000   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 48-72 in LWrist_speed_event is completely within zero chunk 0-211 in LWrist_vert_vel_movement_event
Last non-zero chunk 187-211 in LWrist_speed_event is completely within zero chunk 0-211 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 47-79 in RWrist_speed_event is completely within zero chunk 0-211 in RWrist_vert_vel_movement_event
Last non-zero chunk 185-211 in RWrist_speed_event is completely within zero chunk 0-211 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_17_p1_annotated.csv
We need to merge
We do not need to merge
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        208      226  3466.666667  3766.666667     300.0   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         63       73  1050.000000  1216.666667  166.666667   
3      2        224      242  3733.333333  4033.333333  300.000000   

               column  
1  LElbow_speed_event  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        230      238  3833.333333  3966.666667  133.333333   

             column  
1  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         47       60   783.333333  1000.000000  216.666667   
3      2        232      239  3866.666667  3983.333333  116.666667   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time    duration  \
1      1         47       57  783.333333     950.0  166.666667   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
We do not need to merge
No non-zero rows
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         51       67       850.0  1116.666667  266.666667   
3      2        228      244      3800.0  4066.666667  266.666667   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 41-72 in LWrist_speed_event is completely within zero chunk 0-255 in LWrist_vert_vel_movement_event
Last non-zero chunk 220-248 in LWrist_speed_event is completely within zero chunk 0-255 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 40-73 in RWrist_speed_event is completely within zero chunk 0-255 in RWrist_vert_vel_movement_event
Last non-zero chunk 223-255 in RWrist_speed_event is completely within zero chunk 0-255 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_18_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We do not need to merge
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
3     12        383      389  6383.333333  6483.333333     100.0   

              column  
3  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         98      119  1633.333333  1983.333333     350.0   

             column  
1  RHip_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
3      2        155      158  2583.333333  2633.333333      50.0   

             column  
3  Head_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
3      2        297      306  4950.000000  5100.000000  150.000000   
5      3        334      344  5566.666667  5733.333333  166.666667   
7      4        365      377  6083.333333  6283.333333  200.000000   

               column  
3  RElbow_speed_event  
5  RElbow_speed_event  
7  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
3      3        167      168  2783.333333  2800.000000   16.666667   
7      7        273      296  4550.000000  4933.333333  383.333333   
9      9        343      351  5716.666667  5850.000000  133.333333   

             column  
3  LHip_speed_event  
7  LHip_speed_event  
9  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 79-143 in LWrist_speed_event partially overlaps with zero chunk 0-87 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap

Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 353-375 in RWrist_speed_event is completely within zero chunk 270-389 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_19_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         80       83  1333.333333  1383.333333      50.0   

             column  
1  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         67       70  1116.666667  1166.666667   50.000000   
5      4        247      257  4116.666667  4283.333333  166.666667   
7      5        339      352  5650.000000  5866.666667  216.666667   

                  column  
1  RShoulder_speed_event  
5  RShoulder_speed_event  
7  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
3      2        127      144  2116.666667  2400.000000  283.333333   
5      3        221      229  3683.333333  3816.666667  133.333333   
9      5        330      343  5500.000000  5716.666667  216.666667   

               column  
3  RElbow_speed_event  
5  RElbow_speed_event  
9  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1         98      108  1633.333333    1800.0  166.666667   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 331-344 in LWrist_speed_event is completely within zero chunk 317-352 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 23-83 in RWrist_speed_event partially overlaps with zero chunk 0-25 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 310-339 in RWrist_speed_event partially overlaps with zero chunk 337-352 in RWrist_vert_vel_movement_event
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_1_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_20_p0_annotated.csv
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
3      2        149      156  2483.333333    2600.0  116.666667   

               column  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         63       80      1050.0  1333.333333  283.333333   
3      2        180      193      3000.0  3216.666667  216.666667   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We do not need to merge
We do not need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 23-72 in LWrist_speed_event partially overlaps with zero chunk 0-38 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 27-77 in RWrist_speed_event partially overlaps with zero chunk 0-40 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 183-232 in RWrist_speed_event partially overlaps with zero chunk 226-232 in RWrist_vert_vel_movement_event
We do not need to merge
We do not need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_21_p0_annotated.csv
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
3      2        162      181      2700.0  3016.666667  316.666667   

               column  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 32-62 in LWrist_speed_event partially overlaps with zero chunk 0-34 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 37-69 in RWrist_speed_event partially overlaps with zero chunk 0-38 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 162-200 in RWrist_speed_event partially overlaps with zero chunk 198-215 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_22_p0_annotated.csv
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
   value  start_idx  end_idx   start_time     end_time   duration  \
3      2        272      274  4533.333333  4566.666667  33.333333   

              column  
3  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time   duration  \
1      1         58       60   966.666667    1000.0  33.333333   
3      2        224      225  3733.333333    3750.0  16.666667   

             column  
1  Head_speed_event  
3  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        191      206  3183.333333  3433.333333     250.0   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
3      2        187      193  3116.666667  3216.666667  100.000000   
5      3        249      254  4150.000000  4233.333333   83.333333   

               column  
3  RElbow_speed_event  
5  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        207      215      3450.0  3583.333333  133.333333   

              column  
1  RKnee_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        186      206      3100.0  3433.333333  333.333333   
3      2        255      261      4250.0  4350.000000  100.000000   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 35-101 in LWrist_speed_event partially overlaps with zero chunk 0-38 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 248-261 in LWrist_speed_event is completely within zero chunk 104-274 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 36-102 in RWrist_speed_event partially overlaps with zero chunk 0-38 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 240-262 in RWrist_speed_event is completely within zero chunk 105-274 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_23_p0_annotated.csv
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
3      3        174      190      2900.0  3166.666667  266.666667   

               column  
3  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 31-54 in LWrist_speed_event is completely within zero chunk 0-231 in LWrist_vert_vel_movement_event
Last non-zero chunk 203-231 in LWrist_speed_event is completely within zero chunk 0-231 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 34-86 in RWrist_speed_event partially overlaps with zero chunk 0-55 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 173-208 in RWrist_speed_event partially overlaps with zero chunk 207-231 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_24_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         87      100  1450.000000  1666.666667  216.666667   
3      2        121      137  2016.666667  2283.333333  266.666667   
7      5        257      270  4283.333333  4500.000000  216.666667   

               column  
1  LElbow_speed_event  
3  LElbow_speed_event  
7  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1         86       99  1433.333333    1650.0  216.666667   
5      5        291      294  4850.000000    4900.0   50.000000   

                  column  
1  RShoulder_speed_event  
5  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         50       61   833.333333  1016.666667  183.333333   
3      2        281      295  4683.333333  4916.666667  233.333333   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time  end_time  duration            column
1      1        222      225      3700.0    3750.0      50.0  LHip_speed_event
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
3      3        154      156  2566.666667  2600.000000   33.333333   
5      4        219      222  3650.000000  3700.000000   50.000000   
7      5        287      293  4783.333333  4883.333333  100.000000   

                  column  
3  LShoulder_speed_event  
5  LShoulder_speed_event  
7  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
We need to merge
   value  start_idx  end_idx  start_time     end_time    duration  \
3      9        288      299      4800.0  4983.333333  183.333333   

                           column  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 62-248 in LWrist_speed_event partially overlaps with zero chunk 0-68 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 308-316 in LWrist_speed_event is completely within zero chunk 294-342 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 42-273 in RWrist_speed_event partially overlaps with zero chunk 0-64 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 279-313 in RWrist_speed_event is completely within zero chunk 270-342 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_25_p0_annotated.csv
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
5      4        313      334  5216.666667  5566.666667     350.0   

               column  
5  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
3      3        181      182  3016.666667  3033.333333   16.666667   
5      4        246      263  4100.000000  4383.333333  283.333333   

               column  
3  RElbow_speed_event  
5  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        308      323  5133.333333  5383.333333     250.0   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
We do not need to merge
   value  start_idx  end_idx   start_time  end_time    duration  \
3      2        232      255  3866.666667    4250.0  383.333333   

                           column  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time  end_time  duration  \
3      2        240      255      4000.0    4250.0     250.0   

                           column  
3  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 22-74 in LWrist_speed_event partially overlaps with zero chunk 0-41 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 27-76 in RWrist_speed_event partially overlaps with zero chunk 0-46 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_26_p0_annotated.csv
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
3      3        162      172      2700.0  2866.666667  166.666667   

               column  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         31       35   516.666667   583.333333   66.666667   
5      3        171      185  2850.000000  3083.333333  233.333333   

                  column  
1  RShoulder_speed_event  
5  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        123      131      2050.0  2183.333333  133.333333   

             column  
1  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         73       85  1216.666667  1416.666667  200.000000   
3      2        203      208  3383.333333  3466.666667   83.333333   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 33-47 in LWrist_speed_event is completely within zero chunk 0-67 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 225-230 in LWrist_speed_event is completely within zero chunk 206-249 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 30-49 in RWrist_speed_event is completely within zero chunk 0-56 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 221-230 in RWrist_speed_event is completely within zero chunk 194-249 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
3      3        189      202      3150.0  3366.666667  216.666667   

               column  
3  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_27_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1        191      198  3183.333333    3300.0  116.666667   

              column  
1  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1        113      114  1883.333333  1900.000000  16.666667   
3      2        156      158  2600.000000  2633.333333  33.333333   
5      3        278      279  4633.333333  4650.000000  16.666667   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
5  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time    end_time  duration  \
1      1         46       55  766.666667  916.666667     150.0   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time  duration  \
1      1         35       47  583.333333  783.333333     200.0   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
3      4        255      273      4250.0  4550.000000  300.000000   
5      5        300      316      5000.0  5266.666667  266.666667   

               column  
3  RElbow_speed_event  
5  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        166      184  2766.666667  3066.666667  300.000000   
3      3        299      300  4983.333333  5000.000000   16.666667   

             column  
1  LHip_speed_event  
3  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time    duration  \
1      1         35       48  583.333333     800.0  216.666667   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
We need to merge
   value  start_idx  end_idx  start_time    end_time  duration  \
1      1         34       52  566.666667  866.666667     300.0   

                           column  
1  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         31       44   516.666667   733.333333  216.666667   
5      4        176      198  2933.333333  3300.000000  366.666667   

                           column  
1  LWrist_vert_vel_movement_event  
5  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 27-58 in LWrist_speed_event is completely within zero chunk 0-125 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 96-312 in LWrist_speed_event partially overlaps with zero chunk 267-361 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 30-63 in RWrist_speed_event is completely within zero chunk 0-275 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 104-320 in RWrist_speed_event partially overlaps with zero chunk 318-361 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_28_p1_annotated.csv
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time   end_time   duration  \
0      1          0        4         0.0  66.666667  66.666667   

              column  
0  LHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
5      3        204      212      3400.0  3533.333333  133.333333   

               column  
5  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1         82       86  1366.666667  1433.333333  66.666667   

             column  
1  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
3      3        202      214  3366.666667  3566.666667     200.0   

                  column  
3  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         55       61   916.666667  1016.666667  100.000000   
3      2        207      214  3450.000000  3566.666667  116.666667   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 41-60 in LWrist_speed_event is completely within zero chunk 0-67 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 157-217 in LWrist_speed_event partially overlaps with zero chunk 192-279 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 43-105 in RWrist_speed_event partially overlaps with zero chunk 0-48 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 155-219 in RWrist_speed_event partially overlaps with zero chunk 217-279 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_29_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1         42       45       700.0     750.0      50.0   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
3      2        142      142  2366.666667  2366.666667       0.0   
5      3        188      200  3133.333333  3333.333333     200.0   

                  column  
3  RShoulder_speed_event  
5  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
3      4        179      192  2983.333333    3200.0  216.666667   

                  column  
3  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
We need to merge
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         52       53   866.666667   883.333333   16.666667   
3      2         71       89  1183.333333  1483.333333  300.000000   
5      4        117      138  1950.000000  2300.000000  350.000000   
7      6        180      191  3000.000000  3183.333333  183.333333   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
5  RWrist_vert_vel_movement_event  
7  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         44       58   733.333333   966.666667  233.333333   
3      2         94      116  1566.666667  1933.333333  366.666667   
7      6        193      217  3216.666667  3616.666667  400.000000   

                           column  
1  LWrist_vert_vel_movement_event  
3  LWrist_vert_vel_movement_event  
7  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 29-216 in LWrist_speed_event partially overlaps with zero chunk 0-140 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 253-261 in LWrist_speed_event is completely within zero chunk 169-261 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 31-194 in RWrist_speed_event is completely within zero chunk 0-261 in RWrist_vert_vel_movement_event
Last non-zero chunk 255-261 in RWrist_speed_event is completely within zero chunk 0-261 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_2_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1        118      119  1966.666667  1983.333333  16.666667   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time   duration  \
1      1        121      126  2016.666667    2100.0  83.333333   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1        117      126      1950.0    2100.0     150.0   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 119-144 in LWrist_speed_event is completely within zero chunk 0-165 in LWrist_vert_vel_movement_event
Last non-zero chunk 119-144 in LWrist_speed_event is completely within zero chunk 0-165 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 117-146 in RWrist_speed_event is completely within zero chunk 0-165 in RWrist_vert_vel_movement_event
Last non-zero chunk 117-146 in RWrist_speed_event is completely within zero chunk 0-165 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_30_p1_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time   duration  \
1      1         51       53       850.0  883.333333  33.333333   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         61       71  1016.666667  1183.333333  166.666667   
3      2        175      187  2916.666667  3116.666667  200.000000   

               column  
1  LElbow_speed_event  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         46       65  766.666667  1083.333333  316.666667   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time    duration  \
1      1         46       56  766.666667  933.333333  166.666667   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 37-71 in LWrist_speed_event is completely within zero chunk 0-226 in LWrist_vert_vel_movement_event
Last non-zero chunk 170-190 in LWrist_speed_event is completely within zero chunk 0-226 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 150-193 in RWrist_speed_event partially overlaps with zero chunk 186-226 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      2        170      190  2833.333333  3166.666667  333.333333   

               column  
1  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_31_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
3      2        171      187      2850.0  3116.666667  266.666667   

               column  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
   value  start_idx  end_idx  start_time  end_time   duration  \
1      1         50       51  833.333333     850.0  16.666667   

                           column  
1  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
no overlap
no overlap
no overlap
Last non-zero chunk 156-187 in LWrist_speed_event is completely within zero chunk 120-230 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 167-182 in RWrist_speed_event is completely within zero chunk 0-230 in RWrist_vert_vel_movement_event
Last non-zero chunk 167-182 in RWrist_speed_event is completely within zero chunk 0-230 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_32_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We do not need to merge
We need to merge
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
3      4        223      223  3716.666667  3716.666667       0.0   

              column  
3  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
3      2        232      248  3866.666667  4133.333333  266.666667   

               column  
3  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        152      152  2533.333333  2533.333333       0.0   

             column  
1  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        114      134      1900.0  2233.333333  333.333333   

                           column  
1  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 106-271 in LWrist_speed_event partially overlaps with zero chunk 0-165 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 106-271 in LWrist_speed_event partially overlaps with zero chunk 244-299 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 106-267 in RWrist_speed_event partially overlaps with zero chunk 0-107 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_33_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1          9        9   150.000000   150.000000    0.000000   
5      4        181      193  3016.666667  3216.666667  200.000000   
7      6        219      219  3650.000000  3650.000000    0.000000   
9      7        284      289  4733.333333  4816.666667   83.333333   

              column  
1  RHeel_speed_event  
5  RHeel_speed_event  
7  RHeel_speed_event  
9  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         42       51   700.000000   850.000000  150.000000   
3      2         92       98  1533.333333  1633.333333  100.000000   
9      5        281      283  4683.333333  4716.666667   33.333333   

               column  
1  LElbow_speed_event  
3  LElbow_speed_event  
9  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time   duration  \
1      1         72       74      1200.0  1233.333333  33.333333   
3      2        120      122      2000.0  2033.333333  33.333333   

             column  
1  Head_speed_event  
3  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        109      124  1816.666667  2066.666667     250.0   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1         42       57       700.0     950.0     250.0   
5      4        270      291      4500.0    4850.0     350.0   

               column  
1  RElbow_speed_event  
5  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time    duration  \
1      1         28       51  466.666667     850.0  383.333333   

                           column  
1  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 34-62 in LWrist_speed_event partially overlaps with zero chunk 0-35 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 30-54 in RWrist_speed_event is completely within zero chunk 0-276 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
   value  start_idx  end_idx   start_time     end_time    duration  \
1      2        230      241  3833.333333  4016.666667  183.333333   

               column  
1  RWrist_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_35_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
We need to merge
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         50       55   833.333333   916.666667   83.333333   
3      2         82       88  1366.666667  1466.666667  100.000000   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 37-87 in LWrist_speed_event partially overlaps with zero chunk 0-62 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 236-272 in LWrist_speed_event partially overlaps with zero chunk 265-310 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 43-98 in RWrist_speed_event is completely within zero chunk 0-119 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 236-272 in RWrist_speed_event is completely within zero chunk 168-310 in RWrist_vert_vel_movement_event
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_36_p0_annotated.csv
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         32       43   533.333333   716.666667  183.333333   
3      2         60       72  1000.000000  1200.000000  200.000000   
5      3        254      264  4233.333333  4400.000000  166.666667   

               column  
1  LElbow_speed_event  
3  LElbow_speed_event  
5  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         61       77  1016.666667  1283.333333  266.666667   
3      2        216      219  3600.000000  3650.000000   50.000000   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        237      256      3950.0  4266.666667  316.666667   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
3      2        229      250  3816.666667  4166.666667     350.0   

                           column  
3  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 25-42 in LWrist_speed_event is completely within zero chunk 0-55 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 231-264 in LWrist_speed_event is completely within zero chunk 87-295 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 28-44 in RWrist_speed_event is completely within zero chunk 0-51 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 224-256 in RWrist_speed_event partially overlaps with zero chunk 253-295 in RWrist_vert_vel_movement_event
We need to merge
   value  start_idx  end_idx   start_time     end_time   duration  \
3      4        197      199  3283.333333  3316.666667  33.333333   

               column  
3  RWrist_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_37_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        123      146      2050.0  2433.333333  383.333333   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
3      2        178      184  2966.666667  3066.666667     100.0   

             column  
3  Head_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 29-53 in LWrist_speed_event is completely within zero chunk 0-164 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 31-68 in RWrist_speed_event partially overlaps with zero chunk 0-40 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time   duration  \
1      2        111      115      1850.0  1916.666667  66.666667   

               column  
1  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_38_p0_annotated.csv
We need to merge
We need to merge
We do not need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1         70       78  1166.666667    1300.0  133.333333   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         15       23   250.000000   383.333333  133.333333   
3      2         65       81  1083.333333  1350.000000  266.666667   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         25       34   416.666667   566.666667     150.0   
5      3        125      131  2083.333333  2183.333333     100.0   

               column  
1  RElbow_speed_event  
5  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         18       23   300.000000   383.333333   83.333333   
3      2         65       84  1083.333333  1400.000000  316.666667   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 14-42 in LWrist_speed_event is completely within zero chunk 0-322 in LWrist_vert_vel_movement_event
Last non-zero chunk 266-292 in LWrist_speed_event is completely within zero chunk 0-322 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 15-35 in RWrist_speed_event is completely within zero chunk 0-46 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 146-286 in RWrist_speed_event partially overlaps with zero chunk 283-322 in RWrist_vert_vel_movement_event
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_39_p0_annotated.csv
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1         80       87  1333.333333    1450.0  116.666667   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         30       38   500.000000   633.333333  133.333333   
5      4        186      195  3100.000000  3250.000000  150.000000   
7      5        265      271  4416.666667  4516.666667  100.000000   

               column  
1  RElbow_speed_event  
5  RElbow_speed_event  
7  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         99      115      1650.0  1916.666667  266.666667   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
5      3        187      208  3116.666667  3466.666667     350.0   

                           column  
5  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 23-46 in LWrist_speed_event is completely within zero chunk 0-78 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 23-42 in RWrist_speed_event is completely within zero chunk 0-79 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
   value  start_idx  end_idx   start_time     end_time    duration  \
5      4        188      206  3133.333333  3433.333333  300.000000   
7      5        282      283  4700.000000  4716.666667   16.666667   

               column  
5  RWrist_speed_event  
7  RWrist_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
3      4        189      212      3150.0  3533.333333  383.333333   

               column  
3  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_3_p0_annotated.csv
We do not need to merge
We do not need to merge
We do not need to merge
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time    duration  \
1      1          6       16       100.0  266.666667  166.666667   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
0      1          0       16         0.0   266.666667  266.666667   
2      2        510      523      8500.0  8716.666667  216.666667   

                  column  
0  RShoulder_speed_event  
2  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
0      1          0       19     0.000000   316.666667  316.666667   
2      2        511      519  8516.666667  8650.000000  133.333333   

               column  
0  RElbow_speed_event  
2  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time  end_time   duration  \
1      1         11       12  183.333333     200.0  16.666667   

             column  
1  LHip_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time   end_time   duration  \
0      1          0        4         0.0  66.666667  66.666667   

              column  
0  RKnee_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
0      1          0       15     0.000000     250.0  250.000000   
2      2        520      522  8666.666667    8700.0   33.333333   

                  column  
0  LShoulder_speed_event  
2  LShoulder_speed_event  
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 5-30 in LWrist_speed_event is completely within zero chunk 0-533 in LWrist_vert_vel_movement_event
Last non-zero chunk 504-517 in LWrist_speed_event is completely within zero chunk 0-533 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 0-31 in RWrist_speed_event is completely within zero chunk 0-533 in RWrist_vert_vel_movement_event
Last non-zero chunk 501-532 in RWrist_speed_event is completely within zero chunk 0-533 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_40_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         46       51   766.666667   850.000000   83.333333   
3      2         95      119  1583.333333  1983.333333  400.000000   

             column  
1  LHip_speed_event  
3  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 20-70 in LWrist_speed_event is completely within zero chunk 0-83 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 82-136 in LWrist_speed_event partially overlaps with zero chunk 114-182 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 20-58 in RWrist_speed_event partially overlaps with zero chunk 0-30 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 121-135 in RWrist_speed_event is completely within zero chunk 101-182 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_41_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time   duration  \
1      1        323      324  5383.333333    5400.0  16.666667   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time  duration  \
1      1         28       40  466.666667  666.666667     200.0   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time   duration  \
1      1        156      160      2600.0  2666.666667  66.666667   

                           column  
1  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        149      166  2483.333333  2766.666667  283.333333   

                           column  
1  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 24-43 in LWrist_speed_event is completely within zero chunk 0-364 in LWrist_vert_vel_movement_event
Last non-zero chunk 307-316 in LWrist_speed_event is completely within zero chunk 0-364 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 26-41 in RWrist_speed_event is completely within zero chunk 0-364 in RWrist_vert_vel_movement_event
Last non-zero chunk 311-328 in RWrist_speed_event is completely within zero chunk 0-364 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      2        151      167  2516.666667  2783.333333  266.666667   

               column  
1  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_42_p0_annotated.csv
We need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time    duration  \
1      1         33       46       550.0  766.666667  216.666667   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
3      3        167      181  2783.333333  3016.666667  233.333333   

                  column  
3  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time  end_time  duration  \
5      3        213      213      3550.0    3550.0       0.0   

               column  
5  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 7-21 in LWrist_speed_event is completely within zero chunk 0-265 in LWrist_vert_vel_movement_event
Last non-zero chunk 208-228 in LWrist_speed_event is completely within zero chunk 0-265 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 4-64 in RWrist_speed_event partially overlaps with zero chunk 0-22 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 209-226 in RWrist_speed_event is completely within zero chunk 197-265 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_43_p0_annotated.csv
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        208      220  3466.666667  3666.666667     200.0   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1        209      216  3483.333333    3600.0  116.666667   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 15-68 in LWrist_speed_event partially overlaps with zero chunk 0-28 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 199-221 in LWrist_speed_event is completely within zero chunk 188-253 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 16-67 in RWrist_speed_event partially overlaps with zero chunk 0-28 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 144-223 in RWrist_speed_event partially overlaps with zero chunk 187-253 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_44_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
   value  start_idx  end_idx   start_time     end_time    duration  \
3      2        187      203  3116.666667  3383.333333  266.666667   

              column  
3  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        132      148      2200.0  2466.666667  266.666667   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time   duration  \
5      4        332      333  5533.333333    5550.0  16.666667   

                  column  
5  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         39       52   650.000000   866.666667  216.666667   
3      2         72       85  1200.000000  1416.666667  216.666667   
5      3        231      233  3850.000000  3883.333333   33.333333   
7      4        259      269  4316.666667  4483.333333  166.666667   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
5  RElbow_speed_event  
7  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1        225      225  3750.000000  3750.000000   0.000000   
3      2        254      256  4233.333333  4266.666667  33.333333   

             column  
1  LHip_speed_event  
3  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration  \
5      5        333      333      5550.0    5550.0       0.0   

                  column  
5  LShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 36-49 in LWrist_speed_event is completely within zero chunk 0-80 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 224-277 in LWrist_speed_event partially overlaps with zero chunk 261-333 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 35-89 in RWrist_speed_event partially overlaps with zero chunk 0-56 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 329-333 in RWrist_speed_event is completely within zero chunk 264-333 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration  \
3      3        150      162      2500.0    2700.0     200.0   
5      4        186      189      3100.0    3150.0      50.0   

               column  
3  LWrist_speed_event  
5  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_45_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         48       50   800.000000   833.333333   33.333333   
3      2         97       99  1616.666667  1650.000000   33.333333   
5      3        162      174  2700.000000  2900.000000  200.000000   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
5  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         50       62   833.333333  1033.333333     200.0   
3      2        110      128  1833.333333  2133.333333     300.0   

               column  
1  LElbow_speed_event  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time    duration  \
1      1         50       69  833.333333    1150.0  316.666667   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time    end_time    duration  \
1      1         40       59  666.666667  983.333333  316.666667   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
3      2        193      213  3216.666667    3550.0  333.333333   

                           column  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         35       44   583.333333   733.333333  150.000000   
3      2        203      210  3383.333333  3500.000000  116.666667   

                           column  
1  LWrist_vert_vel_movement_event  
3  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 39-45 in LWrist_speed_event is completely within zero chunk 0-261 in LWrist_vert_vel_movement_event
Last non-zero chunk 152-233 in LWrist_speed_event is completely within zero chunk 0-261 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
Last non-zero chunk 193-234 in RWrist_speed_event is completely within zero chunk 66-261 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_46_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1        154      171  2566.666667    2850.0  283.333333   

              column  
1  LHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         49       55   816.666667   916.666667  100.000000   
3      2         84       86  1400.000000  1433.333333   33.333333   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        107      108  1783.333333  1800.000000   16.666667   
3      2        144      145  2400.000000  2416.666667   16.666667   
5      3        174      181  2900.000000  3016.666667  116.666667   

             column  
1  LHip_speed_event  
3  LHip_speed_event  
5  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         58       70   966.666667  1166.666667  200.000000   
3      2        175      177  2916.666667  2950.000000   33.333333   
5      3        208      228  3466.666667  3800.000000  333.333333   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
5  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        222      229      3700.0  3816.666667  116.666667   

                           column  
1  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 51-63 in LWrist_speed_event is completely within zero chunk 0-339 in LWrist_vert_vel_movement_event
Last non-zero chunk 184-221 in LWrist_speed_event is completely within zero chunk 0-339 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 55-225 in RWrist_speed_event is completely within zero chunk 0-339 in RWrist_vert_vel_movement_event
Last non-zero chunk 55-225 in RWrist_speed_event is completely within zero chunk 0-339 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
1      2        115      132  1916.666667    2200.0  283.333333   

               column  
1  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_47_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        101      125  1683.333333  2083.333333  400.000000   
5      7        335      337  5583.333333  5616.666667   33.333333   

              column  
1  RHeel_speed_event  
5  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         87      101      1450.0  1683.333333  233.333333   
3      2        123      127      2050.0  2116.666667   66.666667   

               column  
1  LElbow_speed_event  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1         94       98  1566.666667  1633.333333  66.666667   
3      2        171      175  2850.000000  2916.666667  66.666667   

             column  
1  Head_speed_event  
3  Head_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
3      3        244      250  4066.666667  4166.666667     100.0   

                  column  
3  LShoulder_speed_event  
We need to turn fake events into 0s
No non-zero rows
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         87       98  1450.000000  1633.333333  183.333333   
3      2        239      251  3983.333333  4183.333333  200.000000   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 79-105 in LWrist_speed_event is completely within zero chunk 0-350 in LWrist_vert_vel_movement_event
Last non-zero chunk 243-247 in LWrist_speed_event is completely within zero chunk 0-350 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 79-222 in RWrist_speed_event is completely within zero chunk 0-350 in RWrist_vert_vel_movement_event
Last non-zero chunk 230-252 in RWrist_speed_event is completely within zero chunk 0-350 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      2        202      206  3366.666667  3433.333333  66.666667   
3      3        243      247  4050.000000  4116.666667  66.666667   

               column  
1  LWrist_speed_event  
3  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_48_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1        137      159  2283.333333    2650.0  366.666667   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
5      3        276      282  4600.000000  4700.000000  100.000000   
7      4        314      322  5233.333333  5366.666667  133.333333   

               column  
5  RElbow_speed_event  
7  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
3      3        306      325      5100.0  5416.666667  316.666667   

                  column  
3  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         56       80   933.333333  1333.333333  400.000000   
5      4        203      223  3383.333333  3716.666667  333.333333   

                           column  
1  RWrist_vert_vel_movement_event  
5  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1        313      321  5216.666667    5350.0  133.333333   

                           column  
1  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 231-247 in LWrist_speed_event is completely within zero chunk 0-353 in LWrist_vert_vel_movement_event
Last non-zero chunk 231-247 in LWrist_speed_event is completely within zero chunk 0-353 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 58-81 in RWrist_speed_event is completely within zero chunk 0-97 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
No non-zero rows
   value  start_idx  end_idx   start_time     end_time    duration  \
3      4        206      223  3433.333333  3716.666667  283.333333   

               column  
3  RWrist_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_49_p1_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         83       83  1383.333333  1383.333333       0.0   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1         64       68  1066.666667  1133.333333  66.666667   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1         61       72  1016.666667    1200.0  183.333333   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 65-81 in LWrist_speed_event is completely within zero chunk 0-225 in LWrist_vert_vel_movement_event
Last non-zero chunk 157-177 in LWrist_speed_event is completely within zero chunk 0-225 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
1      2        157      177  2616.666667    2950.0  333.333333   

               column  
1  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_4_p0_annotated.csv
We do not need to merge
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
   value  start_idx  end_idx   start_time  end_time   duration  \
1      1        274      276  4566.666667    4600.0  33.333333   

              column  
1  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        252      263      4200.0  4383.333333  183.333333   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1        261      264      4350.0    4400.0      50.0   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         60       67  1000.000000  1116.666667  116.666667   
3      2        248      260  4133.333333  4333.333333  200.000000   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1        261      276      4350.0    4600.0     250.0   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 62-79 in LWrist_speed_event is completely within zero chunk 0-276 in LWrist_vert_vel_movement_event
Last non-zero chunk 246-261 in LWrist_speed_event is completely within zero chunk 0-276 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 62-84 in RWrist_speed_event is completely within zero chunk 0-276 in RWrist_vert_vel_movement_event
Last non-zero chunk 244-266 in RWrist_speed_event is completely within zero chunk 0-276 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_50_p1_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        130      137  2166.666667  2283.333333  116.666667   
3      2        186      192  3100.000000  3200.000000  100.000000   
5      3        286      292  4766.666667  4866.666667  100.000000   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
5  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
3      2        183      193      3050.0  3216.666667  166.666667   

               column  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         95      103  1583.333333  1716.666667  133.333333   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1        185      190  3083.333333  3166.666667  83.333333   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         92      104  1533.333333  1733.333333  200.000000   
3      2        242      249  4033.333333  4150.000000  116.666667   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1         88       96  1466.666667    1600.0  133.333333   
3      2        235      252  3916.666667    4200.0  283.333333   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 88-124 in LWrist_speed_event partially overlaps with zero chunk 0-91 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 85-106 in RWrist_speed_event is completely within zero chunk 0-292 in RWrist_vert_vel_movement_event
Last non-zero chunk 231-257 in RWrist_speed_event is completely within zero chunk 0-292 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_51_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time   duration  \
5      6        206      207  3433.333333    3450.0  16.666667   

              column  
5  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         76       79  1266.666667  1316.666667      50.0   

             column  
1  LHip_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1         61       65  1016.666667  1083.333333  66.666667   

              column  
1  RKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap

Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_52_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1          9        9   150.000000   150.000000   0.000000   
5      9        228      229  3800.000000  3816.666667  16.666667   
7     10        265      267  4416.666667  4450.000000  33.333333   

              column  
1  RHeel_speed_event  
5  RHeel_speed_event  
7  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1        121      141  2016.666667    2350.0  333.333333   
3      2        195      210  3250.000000    3500.0  250.000000   
7      4        299      309  4983.333333    5150.0  166.666667   

               column  
1  LElbow_speed_event  
3  LElbow_speed_event  
7  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         58       71   966.666667  1183.333333  216.666667   
5      3        222      236  3700.000000  3933.333333  233.333333   

               column  
1  RElbow_speed_event  
5  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
We need to merge
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         69       77      1150.0  1283.333333  133.333333   

                           column  
1  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        128      140  2133.333333  2333.333333  200.000000   
3      2        197      205  3283.333333  3416.666667  133.333333   

                           column  
1  LWrist_vert_vel_movement_event  
3  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 79-89 in LWrist_speed_event is completely within zero chunk 0-297 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 329-356 in LWrist_speed_event partially overlaps with zero chunk 353-423 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 53-87 in RWrist_speed_event is completely within zero chunk 0-135 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 259-367 in RWrist_speed_event partially overlaps with zero chunk 354-423 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      2        132      134  2200.000000  2233.333333   33.333333   
3      3        196      208  3266.666667  3466.666667  200.000000   

               column  
1  LWrist_speed_event  
3  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_53_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
3      7        177      178  2950.000000  2966.666667   16.666667   
5      8        217      233  3616.666667  3883.333333  266.666667   

              column  
3  RHeel_speed_event  
5  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1         64       75  1066.666667    1250.0  183.333333   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
5      4        238      247  3966.666667  4116.666667     150.0   

             column  
5  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time   duration  \
3      5        262      267  4366.666667    4450.0  83.333333   

                  column  
3  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
3      2        196      216  3266.666667    3600.0  333.333333   

               column  
3  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1         69       78      1150.0    1300.0     150.0   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 52-78 in RWrist_speed_event partially overlaps with zero chunk 0-53 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
We need to merge
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1         54       78       900.0    1300.0     400.0   

               column  
1  RWrist_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_5_p0_annotated.csv
We do not need to merge
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1        170      175  2833.333333  2916.666667  83.333333   

              column  
1  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         70       81  1166.666667  1350.000000  183.333333   
3      2        156      172  2600.000000  2866.666667  266.666667   

               column  
1  LElbow_speed_event  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         67       77  1116.666667  1283.333333  166.666667   
3      2        159      171  2650.000000  2850.000000  200.000000   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         68       77  1133.333333  1283.333333     150.0   
3      2        157      166  2616.666667  2766.666667     150.0   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        175      175  2916.666667  2916.666667       0.0   

              column  
1  RKnee_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         66       76  1100.000000  1266.666667  166.666667   
3      2        154      175  2566.666667  2916.666667  350.000000   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        155      158  2583.333333  2633.333333      50.0   

                           column  
1  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1         97      100  1616.666667  1666.666667  50.000000   
3      2        153      157  2550.000000  2616.666667  66.666667   

                           column  
1  LWrist_vert_vel_movement_event  
3  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 69-104 in LWrist_speed_event is completely within zero chunk 0-175 in LWrist_vert_vel_movement_event
Last non-zero chunk 145-175 in LWrist_speed_event is completely within zero chunk 0-175 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 65-95 in RWrist_speed_event is completely within zero chunk 0-175 in RWrist_vert_vel_movement_event
Last non-zero chunk 137-170 in RWrist_speed_event is completely within zero chunk 0-175 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_6_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        196      203  3266.666667  3383.333333  116.666667   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        188      200  3133.333333  3333.333333     200.0   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 77-84 in LWrist_speed_event is completely within zero chunk 0-223 in LWrist_vert_vel_movement_event
Last non-zero chunk 193-223 in LWrist_speed_event is completely within zero chunk 0-223 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 69-86 in RWrist_speed_event is completely within zero chunk 0-223 in RWrist_vert_vel_movement_event
Last non-zero chunk 191-223 in RWrist_speed_event is completely within zero chunk 0-223 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_7_p0_annotated.csv
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
   value  start_idx  end_idx  start_time     end_time   duration  \
1      1        225      226      3750.0  3766.666667  16.666667   

              column  
1  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         50       64   833.333333  1066.666667  233.333333   
3      2        218      226  3633.333333  3766.666667  133.333333   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         52       62   866.666667  1033.333333  166.666667   
3      2        199      212  3316.666667  3533.333333  216.666667   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1         60       61  1000.000000  1016.666667  16.666667   
3      2        221      226  3683.333333  3766.666667  83.333333   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
We need to turn fake events into 0s
No non-zero rows
   value  start_idx  end_idx  start_time     end_time   duration  \
1      1        204      209      3400.0  3483.333333  83.333333   

                           column  
1  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 51-66 in LWrist_speed_event is completely within zero chunk 0-226 in LWrist_vert_vel_movement_event
Last non-zero chunk 196-226 in LWrist_speed_event is completely within zero chunk 0-226 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 49-72 in RWrist_speed_event is completely within zero chunk 0-226 in RWrist_vert_vel_movement_event
Last non-zero chunk 194-226 in RWrist_speed_event is completely within zero chunk 0-226 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_8_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        119      128  1983.333333  2133.333333     150.0   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        121      131  2016.666667  2183.333333  166.666667   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 0-6 in LWrist_speed_event is completely within zero chunk 0-170 in LWrist_vert_vel_movement_event
Last non-zero chunk 120-144 in LWrist_speed_event is completely within zero chunk 0-170 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 119-147 in RWrist_speed_event is completely within zero chunk 0-170 in RWrist_vert_vel_movement_event
Last non-zero chunk 119-147 in RWrist_speed_event is completely within zero chunk 0-170 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration  \
1      2        120      144      2000.0    2400.0     400.0   

               column  
1  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_9_p1_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time   duration  \
1      1        320      324  5333.333333    5400.0  66.666667   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1        316      324  5266.666667    5400.0  133.333333   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We do not need to merge
No non-zero rows
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        300      311      5000.0  5183.333333  183.333333   

                           column  
1  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 294-317 in LWrist_speed_event is completely within zero chunk 0-324 in LWrist_vert_vel_movement_event
Last non-zero chunk 294-317 in LWrist_speed_event is completely within zero chunk 0-324 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 297-320 in RWrist_speed_event is completely within zero chunk 0-324 in RWrist_vert_vel_movement_event
Last non-zero chunk 297-320 in RWrist_speed_event is completely within zero chunk 0-324 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_tpose_0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_1_tpose_1_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_0_p0_annotated.csv
We need to merge
We do not need to merge
We need to merge
We need to merge
We do not need to merge
We need to merge
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
3      3         92      105  1533.333333  1750.000000  216.666667   
5      5        131      140  2183.333333  2333.333333  150.000000   

              column  
3  RHeel_speed_event  
5  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time  duration  \
1      1         32       38  533.333333  633.333333     100.0   

             column  
1  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time    end_time   duration  \
1      1          8       10  133.333333  166.666667  33.333333   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1          2        9    33.333333   150.000000  116.666667   
3      2        156      157  2600.000000  2616.666667   16.666667   

             column  
1  LHip_speed_event  
3  LHip_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
3      2        176      188  2933.333333  3133.333333     200.0   

              column  
3  RKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We do not need to merge
We do not need to merge
   value  start_idx  end_idx   start_time     end_time  duration  \
3      2         73       76  1216.666667  1266.666667      50.0   

                           column  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 89-159 in LWrist_speed_event partially overlaps with zero chunk 139-188 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 56-161 in RWrist_speed_event partially overlaps with zero chunk 139-188 in RWrist_vert_vel_movement_event
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_100_p1_annotated.csv
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         50       53   833.333333   883.333333   50.000000   
3      2        307      314  5116.666667  5233.333333  116.666667   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1         41       54   683.333333     900.0  216.666667   
3      2        194      195  3233.333333    3250.0   16.666667   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1         51       57   850.000000     950.0  100.000000   
3      2        353      357  5883.333333    5950.0   66.666667   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         41       53   683.333333   883.333333  200.000000   
3      2        189      200  3150.000000  3333.333333  183.333333   
5      3        239      242  3983.333333  4033.333333   50.000000   
7      4        302      311  5033.333333  5183.333333  150.000000   
9      5        344      362  5733.333333  6033.333333  300.000000   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
5  LShoulder_speed_event  
7  LShoulder_speed_event  
9  LShoulder_speed_event  
We need to turn fake events into 0s
No non-zero rows
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         43       55   716.666667   916.666667  200.000000   
3      2        349      362  5816.666667  6033.333333  216.666667   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 32-67 in LWrist_speed_event is completely within zero chunk 0-400 in LWrist_vert_vel_movement_event
Last non-zero chunk 344-364 in LWrist_speed_event is completely within zero chunk 0-400 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 36-62 in RWrist_speed_event is completely within zero chunk 0-400 in RWrist_vert_vel_movement_event
Last non-zero chunk 341-364 in RWrist_speed_event is completely within zero chunk 0-400 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_101_p1_annotated.csv
We need to merge
We need to merge
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time    duration  \
0      1          0        7         0.0  116.666667  116.666667   

              column  
0  LHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time   duration  \
3      6        194      196  3233.333333  3266.666667  33.333333   

              column  
3  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         42       48       700.0   800.000000  100.000000   
3      2        180      196      3000.0  3266.666667  266.666667   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time    end_time  duration  \
1      1         40       43  666.666667  716.666667      50.0   

             column  
1  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
3      2        180      200      3000.0  3333.333333  333.333333   

                  column  
3  LShoulder_speed_event  
We need to turn fake events into 0s
We do not need to merge
No non-zero rows
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         33       41       550.0   683.333333  133.333333   
3      2        183      199      3050.0  3316.666667  266.666667   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 27-47 in LWrist_speed_event is completely within zero chunk 0-211 in LWrist_vert_vel_movement_event
Last non-zero chunk 174-199 in LWrist_speed_event is completely within zero chunk 0-211 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 28-50 in RWrist_speed_event is completely within zero chunk 0-211 in RWrist_vert_vel_movement_event
Last non-zero chunk 178-200 in RWrist_speed_event is completely within zero chunk 0-211 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_102_p1_annotated.csv
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1         40       44   666.666667   733.333333  66.666667   
3      2         85       86  1416.666667  1433.333333  16.666667   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         83       91  1383.333333  1516.666667  133.333333   

             column  
1  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         23       33   383.333333   550.000000  166.666667   
3      2        206      221  3433.333333  3683.333333  250.000000   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         29       41   483.333333   683.333333  200.000000   
3      2        212      219  3533.333333  3650.000000  116.666667   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         22       34   366.666667   566.666667  200.000000   
3      2        208      222  3466.666667  3700.000000  233.333333   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
We need to turn fake events into 0s
We do not need to merge
No non-zero rows
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         26       40   433.333333   666.666667  233.333333   
3      2        210      225  3500.000000  3750.000000  250.000000   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 18-39 in LWrist_speed_event is completely within zero chunk 0-239 in LWrist_vert_vel_movement_event
Last non-zero chunk 201-223 in LWrist_speed_event is completely within zero chunk 0-239 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 17-47 in RWrist_speed_event is completely within zero chunk 0-239 in RWrist_vert_vel_movement_event
Last non-zero chunk 206-228 in RWrist_speed_event is completely within zero chunk 0-239 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_103_p1_annotated.csv
We need to merge
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time   duration  \
1      1        123      124      2050.0  2066.666667  16.666667   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time   duration  \
1      1        168      172      2800.0  2866.666667  66.666667   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time    duration  \
1      1         26       37  433.333333  616.666667  183.333333   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         29       38   483.333333   633.333333  150.000000   
3      2        134      136  2233.333333  2266.666667   33.333333   
5      3        293      302  4883.333333  5033.333333  150.000000   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
5  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         26       37   433.333333   616.666667  183.333333   
3      2        129      145  2150.000000  2416.666667  266.666667   
5      3        202      219  3366.666667  3650.000000  283.333333   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
5  LShoulder_speed_event  
We need to turn fake events into 0s
No non-zero rows
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         20       29   333.333333   483.333333     150.0   
3      2        289      301  4816.666667  5016.666667     200.0   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 17-42 in LWrist_speed_event is completely within zero chunk 0-324 in LWrist_vert_vel_movement_event
Last non-zero chunk 286-307 in LWrist_speed_event is completely within zero chunk 0-324 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 16-38 in RWrist_speed_event is completely within zero chunk 0-324 in RWrist_vert_vel_movement_event
Last non-zero chunk 282-306 in RWrist_speed_event is completely within zero chunk 0-324 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_104_p1_annotated.csv
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         51       52   850.000000   866.666667   16.666667   
3      2        112      133  1866.666667  2216.666667  350.000000   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         74       82  1233.333333  1366.666667  133.333333   

             column  
1  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         38       49   633.333333   816.666667  183.333333   
3      2        118      127  1966.666667  2116.666667  150.000000   
5      4        162      180  2700.000000  3000.000000  300.000000   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
5  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time   duration  \
1      1         39       43       650.0   716.666667  66.666667   
3      2        171      176      2850.0  2933.333333  83.333333   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         40       49   666.666667   816.666667  150.000000   
3      2         83       93  1383.333333  1550.000000  166.666667   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
We need to turn fake events into 0s
We do not need to merge
No non-zero rows
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         33       47   550.000000   783.333333  233.333333   
3      2        170      186  2833.333333  3100.000000  266.666667   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 29-52 in LWrist_speed_event is completely within zero chunk 0-194 in LWrist_vert_vel_movement_event
Last non-zero chunk 166-187 in LWrist_speed_event is completely within zero chunk 0-194 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 30-54 in RWrist_speed_event is completely within zero chunk 0-194 in RWrist_vert_vel_movement_event
Last non-zero chunk 165-188 in RWrist_speed_event is completely within zero chunk 0-194 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_105_p1_annotated.csv
We do not need to merge
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        160      163  2666.666667  2716.666667      50.0   

              column  
1  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time   duration  \
1      1        152      156  2533.333333    2600.0  66.666667   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration            column
3      2        159      165      2650.0    2750.0     100.0  Head_speed_event
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time    end_time    duration  \
1      1         35       46  583.333333  766.666667  183.333333   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time    end_time    duration  \
1      1         42       56       700.0  933.333333  233.333333   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        152      164  2533.333333  2733.333333     200.0   

             column  
1  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time    duration  \
1      1         35       45  583.333333     750.0  166.666667   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
We do not need to merge
No non-zero rows
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         33       40   550.000000   666.666667  116.666667   
3      2        160      175  2666.666667  2916.666667  250.000000   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 26-46 in LWrist_speed_event is completely within zero chunk 0-184 in LWrist_vert_vel_movement_event
Last non-zero chunk 148-175 in LWrist_speed_event is completely within zero chunk 0-184 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 29-53 in RWrist_speed_event is completely within zero chunk 0-184 in RWrist_vert_vel_movement_event
Last non-zero chunk 156-176 in RWrist_speed_event is completely within zero chunk 0-184 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_106_p1_annotated.csv
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        143      154  2383.333333  2566.666667  183.333333   

              column  
1  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        147      154      2450.0  2566.666667  116.666667   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time    duration  \
1      1         32       46  533.333333  766.666667  233.333333   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         35       48   583.333333   800.000000  216.666667   
3      2        141      154  2350.000000  2566.666667  216.666667   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1        140      145  2333.333333  2416.666667  83.333333   

             column  
1  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         31       48   516.666667   800.000000  283.333333   
3      2        135      154  2250.000000  2566.666667  316.666667   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         28       40   466.666667   666.666667  200.000000   
3      2        135      154  2250.000000  2566.666667  316.666667   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        146      154  2433.333333  2566.666667  133.333333   

                           column  
1  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 25-47 in LWrist_speed_event is completely within zero chunk 0-154 in LWrist_vert_vel_movement_event
Last non-zero chunk 135-154 in LWrist_speed_event is completely within zero chunk 0-154 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 25-47 in RWrist_speed_event is completely within zero chunk 0-154 in RWrist_vert_vel_movement_event
Last non-zero chunk 132-154 in RWrist_speed_event is completely within zero chunk 0-154 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_107_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
3      4        246      254      4100.0  4233.333333  133.333333   

              column  
3  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        165      172  2750.000000  2866.666667  116.666667   
3      2        196      208  3266.666667  3466.666667  200.000000   

               column  
1  LElbow_speed_event  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1        205      206  3416.666667  3433.333333  16.666667   

             column  
1  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1        195      207      3250.0    3450.0     200.0   

                           column  
1  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 194-218 in LWrist_speed_event is completely within zero chunk 0-266 in LWrist_vert_vel_movement_event
Last non-zero chunk 194-218 in LWrist_speed_event is completely within zero chunk 0-266 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 191-215 in RWrist_speed_event is completely within zero chunk 0-266 in RWrist_vert_vel_movement_event
Last non-zero chunk 191-215 in RWrist_speed_event is completely within zero chunk 0-266 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_108_p1_annotated.csv
We do not need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time   duration  \
1      1        203      204  3383.333333    3400.0  16.666667   
3      2        231      231  3850.000000    3850.0   0.000000   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        172      178  2866.666667  2966.666667     100.0   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1         70       74  1166.666667  1233.333333  66.666667   
3      2        111      114  1850.000000  1900.000000  50.000000   

             column  
1  Head_speed_event  
3  Head_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time   duration  \
1      1         84       88      1400.0  1466.666667  66.666667   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         73       89  1216.666667  1483.333333  266.666667   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1         73       84  1216.666667    1400.0  183.333333   
3      2        229      243  3816.666667    4050.0  233.333333   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1        235      243  3916.666667    4050.0  133.333333   

                           column  
1  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 69-94 in LWrist_speed_event is completely within zero chunk 0-243 in LWrist_vert_vel_movement_event
Last non-zero chunk 225-243 in LWrist_speed_event is completely within zero chunk 0-243 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 69-93 in RWrist_speed_event is completely within zero chunk 0-243 in RWrist_vert_vel_movement_event
Last non-zero chunk 219-243 in RWrist_speed_event is completely within zero chunk 0-243 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_109_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We do not need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
   value  start_idx  end_idx   start_time  end_time   duration  \
1      1        365      369  6083.333333    6150.0  66.666667   

              column  
1  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
    value  start_idx  end_idx   start_time     end_time    duration  \
1       1         56       58   933.333333   966.666667   33.333333   
3       2         94      100  1566.666667  1666.666667  100.000000   
9      10        287      298  4783.333333  4966.666667  183.333333   
11     11        334      338  5566.666667  5633.333333   66.666667   

               column  
1   RHeel_speed_event  
3   RHeel_speed_event  
9   RHeel_speed_event  
11  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        169      170  2816.666667  2833.333333   16.666667   
3      2        203      210  3383.333333  3500.000000  116.666667   

               column  
1  LElbow_speed_event  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         57       66   950.000000  1100.000000  150.000000   
3      2        100      107  1666.666667  1783.333333  116.666667   

             column  
1  Head_speed_event  
3  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1         42       57       700.0     950.0     250.0   
5      4        285      303      4750.0    5050.0     300.0   

                  column  
1  RShoulder_speed_event  
5  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         43       56   716.666667   933.333333  216.666667   
3      2        352      369  5866.666667  6150.000000  283.333333   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1         47       48   783.333333   800.000000  16.666667   
3      2        353      355  5883.333333  5916.666667  33.333333   

             column  
1  LHip_speed_event  
3  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
5      5        287      291  4783.333333    4850.0   66.666667   
7      6        347      363  5783.333333    6050.0  266.666667   

                  column  
5  LShoulder_speed_event  
7  LShoulder_speed_event  
We need to turn fake events into 0s
We do not need to merge
No non-zero rows
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1         39       51       650.0     850.0     200.0   
3      2        345      366      5750.0    6100.0     350.0   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 34-58 in LWrist_speed_event is completely within zero chunk 0-369 in LWrist_vert_vel_movement_event
Last non-zero chunk 337-365 in LWrist_speed_event is completely within zero chunk 0-369 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 36-60 in RWrist_speed_event is completely within zero chunk 0-369 in RWrist_vert_vel_movement_event
Last non-zero chunk 339-369 in RWrist_speed_event is completely within zero chunk 0-369 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time   duration  \
1      2        169      174  2816.666667    2900.0  83.333333   

               column  
1  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_10_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
3     13        314      314  5233.333333  5233.333333       0.0   

              column  
3  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
5      3        180      187  3000.000000  3116.666667  116.666667   
7      4        239      243  3983.333333  4050.000000   66.666667   

             column  
5  Head_speed_event  
7  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
4      4        291      302  4850.000000  5033.333333  183.333333   
6      5        341      347  5683.333333  5783.333333  100.000000   

                  column  
4  RShoulder_speed_event  
6  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
0      1          0       16         0.0   266.666667  266.666667   
4      6        345      347      5750.0  5783.333333   33.333333   

                  column  
0  LShoulder_speed_event  
4  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
no overlap
no overlap
no overlap
Last non-zero chunk 0-292 in LWrist_speed_event partially overlaps with zero chunk 280-347 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
Last non-zero chunk 345-347 in RWrist_speed_event is completely within zero chunk 277-347 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_110_p1_annotated.csv
We need to merge
We need to merge
We do not need to merge
We do not need to merge
We need to merge
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         79       94  1316.666667  1566.666667  250.000000   
3      3        171      176  2850.000000  2933.333333   83.333333   
5      4        201      215  3350.000000  3583.333333  233.333333   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
5  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         39       46   650.000000   766.666667  116.666667   
3      2        135      137  2250.000000  2283.333333   33.333333   
5      3        271      283  4516.666667  4716.666667  200.000000   

               column  
1  LElbow_speed_event  
3  LElbow_speed_event  
5  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time    duration  \
1      1         30       44       500.0  733.333333  233.333333   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         90       95  1500.000000  1583.333333   83.333333   
3      2        115      120  1916.666667  2000.000000   83.333333   
5      3        204      224  3400.000000  3733.333333  333.333333   
7      4        275      296  4583.333333  4933.333333  350.000000   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
5  RElbow_speed_event  
7  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time  end_time  duration            column
1      1         39       39       650.0     650.0       0.0  LHip_speed_event
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        293      302  4883.333333  5033.333333     150.0   

              column  
1  RKnee_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time    end_time  duration  \
1      1         29       44  483.333333  733.333333     250.0   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
We do not need to merge
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        279      295      4650.0  4916.666667  266.666667   

                           column  
1  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time    end_time  duration  \
1      1         29       32  483.333333  533.333333      50.0   

                           column  
1  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 22-65 in LWrist_speed_event is completely within zero chunk 0-302 in LWrist_vert_vel_movement_event
Last non-zero chunk 268-297 in LWrist_speed_event is completely within zero chunk 0-302 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 23-44 in RWrist_speed_event is completely within zero chunk 0-302 in RWrist_vert_vel_movement_event
Last non-zero chunk 273-298 in RWrist_speed_event is completely within zero chunk 0-302 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_111_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        184      189  3066.666667  3150.000000   83.333333   
3      2        338      344  5633.333333  5733.333333  100.000000   

              column  
1  LKnee_speed_event  
3  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
5      9        273      273  4550.000000  4550.000000   0.000000   
7     10        308      310  5133.333333  5166.666667  33.333333   

              column  
5  RHeel_speed_event  
7  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         54       64   900.000000  1066.666667  166.666667   
5      3        218      228  3633.333333  3800.000000  166.666667   

               column  
1  LElbow_speed_event  
5  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         50       61   833.333333  1016.666667  183.333333   
3      2        107      110  1783.333333  1833.333333   50.000000   
7      5        219      235  3650.000000  3916.666667  266.666667   
9      6        332      344  5533.333333  5733.333333  200.000000   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
7  RShoulder_speed_event  
9  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         61       70  1016.666667  1166.666667     150.0   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        144      146  2400.000000  2433.333333   33.333333   
3      2        338      344  5633.333333  5733.333333  100.000000   

             column  
1  LHip_speed_event  
3  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         49       62   816.666667  1033.333333  216.666667   
5      4        221      237  3683.333333  3950.000000  266.666667   
7      5        332      344  5533.333333  5733.333333  200.000000   

                  column  
1  LShoulder_speed_event  
5  LShoulder_speed_event  
7  LShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        232      241  3866.666667  4016.666667  150.000000   
3      2        328      344  5466.666667  5733.333333  266.666667   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 41-64 in LWrist_speed_event is completely within zero chunk 0-139 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 324-344 in LWrist_speed_event is completely within zero chunk 218-344 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 44-68 in RWrist_speed_event is completely within zero chunk 0-344 in RWrist_vert_vel_movement_event
Last non-zero chunk 326-344 in RWrist_speed_event is completely within zero chunk 0-344 in RWrist_vert_vel_movement_event
We need to merge
   value  start_idx  end_idx   start_time  end_time    duration  \
1      2        220      243  3666.666667    4050.0  383.333333   

               column  
1  RWrist_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_112_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        203      215  3383.333333  3583.333333     200.0   

              column  
1  LHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         16       25   266.666667   416.666667  150.000000   
3      3         58       59   966.666667   983.333333   16.666667   
7     11        222      234  3700.000000  3900.000000  200.000000   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
7  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        243      260      4050.0  4333.333333  283.333333   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time    duration  \
1      1         41       55  683.333333  916.666667  233.333333   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         54       59   900.000000   983.333333   83.333333   
3      2         81       91  1350.000000  1516.666667  166.666667   
5      3        163      171  2716.666667  2850.000000  133.333333   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
5  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         38       50   633.333333   833.333333  200.000000   
3      2        248      264  4133.333333  4400.000000  266.666667   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        255      262      4250.0  4366.666667  116.666667   

                           column  
1  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 31-57 in LWrist_speed_event is completely within zero chunk 0-290 in LWrist_vert_vel_movement_event
Last non-zero chunk 236-264 in LWrist_speed_event is completely within zero chunk 0-290 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 32-60 in RWrist_speed_event is completely within zero chunk 0-290 in RWrist_vert_vel_movement_event
Last non-zero chunk 221-264 in RWrist_speed_event is completely within zero chunk 0-290 in RWrist_vert_vel_movement_event
We need to merge
   value  start_idx  end_idx   start_time     end_time    duration  \
1      2        132      143  2200.000000  2383.333333  183.333333   
3      3        181      205  3016.666667  3416.666667  400.000000   

               column  
1  RWrist_speed_event  
3  RWrist_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time   duration  \
1      2         87       89      1450.0  1483.333333  33.333333   

               column  
1  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_113_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        170      191  2833.333333  3183.333333  350.000000   
3      3        247      248  4116.666667  4133.333333   16.666667   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time    duration  \
1      1         37       48  616.666667     800.0  183.333333   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time    duration  \
1      1         37       47  616.666667  783.333333  166.666667   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         35       42   583.333333   700.000000  116.666667   
3      2        200      214  3333.333333  3566.666667  233.333333   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1        212      214  3533.333333  3566.666667  33.333333   

                           column  
1  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 28-50 in LWrist_speed_event is completely within zero chunk 0-273 in LWrist_vert_vel_movement_event
Last non-zero chunk 198-217 in LWrist_speed_event is completely within zero chunk 0-273 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 31-53 in RWrist_speed_event is completely within zero chunk 0-273 in RWrist_vert_vel_movement_event
Last non-zero chunk 194-216 in RWrist_speed_event is completely within zero chunk 0-273 in RWrist_vert_vel_movement_event
   value  start_idx  end_idx   start_time  end_time    duration  \
1      2        194      216  3233.333333    3600.0  366.666667   

               column  
1  RWrist_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
1      2        198      217      3300.0  3616.666667  316.666667   

               column  
1  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_11_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         65       73  1083.333333  1216.666667  133.333333   
3      3        106      110  1766.666667  1833.333333   66.666667   
5      4        172      179  2866.666667  2983.333333  116.666667   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
5  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1        173      175  2883.333333  2916.666667  33.333333   

             column  
1  LHip_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        168      185      2800.0  3083.333333  283.333333   

              column  
1  RKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         48       55   800.000000   916.666667  116.666667   
7      4        260      281  4333.333333  4683.333333  350.000000   

                           column  
1  RWrist_vert_vel_movement_event  
7  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 13-78 in LWrist_speed_event partially overlaps with zero chunk 0-29 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 96-298 in LWrist_speed_event partially overlaps with zero chunk 292-329 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 18-67 in RWrist_speed_event is completely within zero chunk 0-93 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 86-300 in RWrist_speed_event partially overlaps with zero chunk 238-329 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_12_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         76       76  1266.666667  1266.666667       0.0   
3      2        127      127  2116.666667  2116.666667       0.0   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time  duration  \
1      1         40       46  666.666667  766.666667     100.0   

             column  
1  Head_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 17-331 in LWrist_speed_event partially overlaps with zero chunk 0-37 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 17-331 in LWrist_speed_event partially overlaps with zero chunk 322-364 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 15-85 in RWrist_speed_event partially overlaps with zero chunk 0-39 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 97-337 in RWrist_speed_event partially overlaps with zero chunk 321-364 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_13_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time  duration  \
1      1         40       40  666.666667  666.666667       0.0   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
3      2        103      114  1716.666667    1900.0  183.333333   

               column  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
3      3        256      260  4266.666667  4333.333333  66.666667   

             column  
3  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         79       91  1316.666667  1516.666667     200.0   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time   duration  \
1      1         41       43  683.333333  716.666667  33.333333   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
3      3        172      194  2866.666667  3233.333333  366.666667   

                           column  
3  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 21-66 in LWrist_speed_event partially overlaps with zero chunk 0-25 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 171-296 in LWrist_speed_event partially overlaps with zero chunk 280-319 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 171-292 in RWrist_speed_event partially overlaps with zero chunk 279-319 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_14_p0_annotated.csv
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         93      104  1550.000000  1733.333333  183.333333   
3      2        334      342  5566.666667  5700.000000  133.333333   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         16       23   266.666667   383.333333  116.666667   
3      2         85      105  1416.666667  1750.000000  333.333333   
5      3        232      234  3866.666667  3900.000000   33.333333   
9      5        330      342  5500.000000  5700.000000  200.000000   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
5  RElbow_speed_event  
9  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
3      2        228      242  3800.000000  4033.333333  233.333333   
5      3        274      287  4566.666667  4783.333333  216.666667   

                           column  
3  RWrist_vert_vel_movement_event  
5  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         73       96  1216.666667  1600.000000  383.333333   
3      2        330      346  5500.000000  5766.666667  266.666667   

                           column  
1  LWrist_vert_vel_movement_event  
3  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 18-34 in LWrist_speed_event is completely within zero chunk 0-391 in LWrist_vert_vel_movement_event
Last non-zero chunk 302-350 in LWrist_speed_event is completely within zero chunk 0-391 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 13-31 in RWrist_speed_event is completely within zero chunk 0-67 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      2         77       95  1283.333333  1583.333333  300.000000   
3      3        242      262  4033.333333  4366.666667  333.333333   

               column  
1  LWrist_speed_event  
3  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_15_p0_annotated.csv
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
3      2        109      119  1816.666667  1983.333333  166.666667   

               column  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time    duration  \
1      1         37       54  616.666667     900.0  283.333333   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         41       64  683.333333  1066.666667  383.333333   

                           column  
1  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         39       62       650.0  1033.333333  383.333333   

                           column  
1  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 34-63 in LWrist_speed_event is completely within zero chunk 0-95 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 31-63 in RWrist_speed_event is completely within zero chunk 0-95 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_16_p0_annotated.csv
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time   duration  \
1      1         60       61      1000.0  1016.666667  16.666667   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         86       92  1433.333333  1533.333333     100.0   

             column  
1  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time  duration  \
1      1         50       71  833.333333  1183.333333     350.0   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time   duration  \
1      1         56       61  933.333333  1016.666667  83.333333   

              column  
1  RKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
3      2        166      186  2766.666667    3100.0  333.333333   

                           column  
3  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 21-38 in LWrist_speed_event is completely within zero chunk 0-43 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 169-183 in LWrist_speed_event is completely within zero chunk 71-218 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 22-184 in RWrist_speed_event partially overlaps with zero chunk 0-35 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 22-184 in RWrist_speed_event partially overlaps with zero chunk 146-218 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
3      4        144      152      2400.0  2533.333333  133.333333   

               column  
3  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_17_p0_annotated.csv
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time   duration  \
1      1         56       58  933.333333  966.666667  33.333333   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time    end_time  duration  \
1      1         44       56  733.333333  933.333333     200.0   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time   duration  \
1      1        178      183  2966.666667    3050.0  83.333333   

             column  
1  Head_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        115      121  1916.666667  2016.666667     100.0   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 40-47 in LWrist_speed_event is completely within zero chunk 0-95 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 17-86 in RWrist_speed_event partially overlaps with zero chunk 0-29 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 162-247 in RWrist_speed_event partially overlaps with zero chunk 245-274 in RWrist_vert_vel_movement_event
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_18_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         91       91  1516.666667  1516.666667       0.0   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        151      151  2516.666667  2516.666667       0.0   

             column  
1  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time   duration  \
3      2        201      205      3350.0  3416.666667  66.666667   

                  column  
3  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         22       34   366.666667   566.666667     200.0   
3      2         63       81  1050.000000  1350.000000     300.0   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
We need to merge
   value  start_idx  end_idx   start_time     end_time  duration  \
3      4        287      305  4783.333333  5083.333333     300.0   

                           column  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
3      2        110      121  1833.333333  2016.666667  183.333333   
5      3        198      207  3300.000000  3450.000000  150.000000   

                           column  
3  LWrist_vert_vel_movement_event  
5  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 320-419 in LWrist_speed_event partially overlaps with zero chunk 398-448 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 27-43 in RWrist_speed_event is completely within zero chunk 0-77 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 322-416 in RWrist_speed_event partially overlaps with zero chunk 410-448 in RWrist_vert_vel_movement_event
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_19_p1_annotated.csv
We need to merge
We do not need to merge
We need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1        116      121  1933.333333  2016.666667  83.333333   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
3      3        215      236  3583.333333  3933.333333     350.0   

               column  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         75       79      1250.0  1316.666667   66.666667   
5      4        228      237      3800.0  3950.000000  150.000000   

                  column  
1  RShoulder_speed_event  
5  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        122      142  2033.333333  2366.666667  333.333333   
3      2        221      233  3683.333333  3883.333333  200.000000   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        222      238      3700.0  3966.666667  266.666667   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
We do not need to merge
We need to merge
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1         68       81  1133.333333    1350.0  216.666667   

                           column  
1  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 63-86 in LWrist_speed_event is completely within zero chunk 0-108 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 217-249 in LWrist_speed_event partially overlaps with zero chunk 248-263 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 65-87 in RWrist_speed_event is completely within zero chunk 0-105 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We do not need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_1_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
3      4        129      137      2150.0  2283.333333  133.333333   

              column  
3  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
3      2        108      115      1800.0  1916.666667  116.666667   

               column  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time   duration  \
3      5        218      222  3633.333333    3700.0  66.666667   

                  column  
3  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         48       62   800.000000  1033.333333  233.333333   
3      2        122      132  2033.333333  2200.000000  166.666667   

              column  
1  RKnee_speed_event  
3  RKnee_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time    end_time    duration  \
0      1          0       13         0.0  216.666667  216.666667   

                  column  
0  LShoulder_speed_event  
We need to turn fake events into 0s
We do not need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time   duration  \
1      1        150      154      2500.0  2566.666667  66.666667   
3      2        177      177      2950.0  2950.000000   0.000000   

                           column  
1  LWrist_vert_vel_movement_event  
3  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 4-34 in LWrist_speed_event is completely within zero chunk 0-222 in LWrist_vert_vel_movement_event
Last non-zero chunk 133-179 in LWrist_speed_event is completely within zero chunk 0-222 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_20_p1_annotated.csv
We need to merge
We do not need to merge
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1         41       51   683.333333     850.0  166.666667   
3      2         80       90  1333.333333    1500.0  166.666667   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
0      1          0       20     0.000000   333.333333  333.333333   
2      2        133      143  2216.666667  2383.333333  166.666667   
6      4        207      217  3450.000000  3616.666667  166.666667   

               column  
0  LElbow_speed_event  
2  LElbow_speed_event  
6  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration            column
1      1        117      126      1950.0    2100.0     150.0  Head_speed_event
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time  end_time  duration  \
0      1          0       12         0.0     200.0     200.0   

                  column  
0  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
2      2        132      145      2200.0  2416.666667  216.666667   
4      3        213      230      3550.0  3833.333333  283.333333   

               column  
2  RElbow_speed_event  
4  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time   duration  \
1      1         22       26  366.666667  433.333333  66.666667   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
0      1          0       14         0.0   233.333333  233.333333   
2      2         93      109      1550.0  1816.666667  266.666667   
4      3        237      241      3950.0  4016.666667   66.666667   

                           column  
0  RWrist_vert_vel_movement_event  
2  RWrist_vert_vel_movement_event  
4  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
0      1          0       17     0.000000   283.333333  283.333333   
2      2         92      105  1533.333333  1750.000000  216.666667   

                           column  
0  LWrist_vert_vel_movement_event  
2  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 0-29 in LWrist_speed_event is completely within zero chunk 0-156 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 205-247 in LWrist_speed_event partially overlaps with zero chunk 236-268 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 0-31 in RWrist_speed_event is completely within zero chunk 0-268 in RWrist_vert_vel_movement_event
Last non-zero chunk 220-248 in RWrist_speed_event is completely within zero chunk 0-268 in RWrist_vert_vel_movement_event
   value  start_idx  end_idx   start_time     end_time    duration  \
3      3        140      148  2333.333333  2466.666667  133.333333   

               column  
3  RWrist_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_21_p1_annotated.csv
We do not need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time   duration  \
1      1        257      258  4283.333333    4300.0  16.666667   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
3      3        274      280  4566.666667  4666.666667     100.0   

             column  
3  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
3      3        230      242  3833.333333  4033.333333     200.0   

                  column  
3  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         97      106  1616.666667  1766.666667     150.0   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
3      2        147      154      2450.0  2566.666667  116.666667   

                           column  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap

Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 221-256 in RWrist_speed_event partially overlaps with zero chunk 255-280 in RWrist_vert_vel_movement_event
   value  start_idx  end_idx  start_time  end_time  duration  \
3      2        135      153      2250.0    2550.0     300.0   

               column  
3  RWrist_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_22_p1_annotated.csv
We need to merge
We need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time   duration  \
1      1        368      369  6133.333333    6150.0  16.666667   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        204      225  3400.000000  3750.000000     350.0   
3      2        356      377  5933.333333  6283.333333     350.0   

               column  
1  LElbow_speed_event  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time    duration  \
1      1         25       38  416.666667  633.333333  216.666667   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
3      2        302      308  5033.333333  5133.333333     100.0   

               column  
3  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time  duration  \
1      1         25       43  416.666667  716.666667     300.0   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
We do not need to merge
We do not need to merge
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         30       43       500.0   716.666667  216.666667   
3      2        132      145      2200.0  2416.666667  216.666667   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 26-46 in LWrist_speed_event is completely within zero chunk 0-191 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 24-45 in RWrist_speed_event is completely within zero chunk 0-190 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We do not need to merge
We do not need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_23_p1_annotated.csv
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
3      3        126      139      2100.0  2316.666667  216.666667   
5      5        291      293      4850.0  4883.333333   33.333333   

              column  
3  RHeel_speed_event  
5  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
3      2        168      185      2800.0  3083.333333  283.333333   
5      3        252      268      4200.0  4466.666667  266.666667   

                  column  
3  RShoulder_speed_event  
5  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
5      4        253      267  4216.666667    4450.0  233.333333   

                  column  
5  LShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time  end_time   duration  \
1      1         46       51  766.666667     850.0  83.333333   

                           column  
1  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         98      107  1633.333333  1783.333333     150.0   

                           column  
1  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 84-110 in LWrist_speed_event is completely within zero chunk 0-145 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 231-278 in LWrist_speed_event partially overlaps with zero chunk 275-300 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 85-197 in RWrist_speed_event partially overlaps with zero chunk 0-90 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 232-281 in RWrist_speed_event partially overlaps with zero chunk 280-300 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_24_p1_annotated.csv
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
3      4        280      286  4666.666667  4766.666667     100.0   

              column  
3  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         93      100  1550.000000  1666.666667  116.666667   
3      2        260      265  4333.333333  4416.666667   83.333333   
5      3        312      312  5200.000000  5200.000000    0.000000   

               column  
1  LElbow_speed_event  
3  LElbow_speed_event  
5  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         84      100      1400.0  1666.666667  266.666667   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
no overlap
no overlap
no overlap
no overlap

Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
We do not need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
3      2        286      302  4766.666667  5033.333333  266.666667   

               column  
3  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_25_p1_annotated.csv
We need to merge
We need to merge
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1          9        9   150.000000   150.000000    0.000000   
3      2         91      113  1516.666667  1883.333333  366.666667   
5      4        214      216  3566.666667  3600.000000   33.333333   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
5  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1         61       69  1016.666667    1150.0  133.333333   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time   duration  \
3      2        252      257      4200.0  4283.333333  83.333333   

             column  
3  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        125      131  2083.333333  2183.333333  100.000000   
3      2        165      184  2750.000000  3066.666667  316.666667   
5      3        262      283  4366.666667  4716.666667  350.000000   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
5  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         56       66   933.333333  1100.000000  166.666667   
5      3        164      184  2733.333333  3066.666667  333.333333   

               column  
1  RElbow_speed_event  
5  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         71       73  1183.333333  1216.666667   33.333333   
3      2        124      131  2066.666667  2183.333333  116.666667   
5      3        169      179  2816.666667  2983.333333  166.666667   
7      4        267      280  4450.000000  4666.666667  216.666667   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
5  LShoulder_speed_event  
7  LShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         49       65   816.666667  1083.333333  266.666667   
5      3        179      200  2983.333333  3333.333333  350.000000   
7      4        262      284  4366.666667  4733.333333  366.666667   

                           column  
1  RWrist_vert_vel_movement_event  
5  RWrist_vert_vel_movement_event  
7  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1         51       66   850.000000    1100.0  250.000000   
5      3        182      198  3033.333333    3300.0  266.666667   

                           column  
1  LWrist_vert_vel_movement_event  
5  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 54-69 in LWrist_speed_event is completely within zero chunk 0-110 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 245-285 in LWrist_speed_event partially overlaps with zero chunk 276-307 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 53-69 in RWrist_speed_event is completely within zero chunk 0-107 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 245-290 in RWrist_speed_event is completely within zero chunk 151-307 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_26_p1_annotated.csv
We need to merge
We do not need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         78       79  1300.000000  1316.666667   16.666667   
3      2        171      190  2850.000000  3166.666667  316.666667   
5      4        244      248  4066.666667  4133.333333   66.666667   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
5  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
3      2        308      316  5133.333333  5266.666667  133.333333   

               column  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         79       92  1316.666667  1533.333333  216.666667   
3      2        180      194  3000.000000  3233.333333  233.333333   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         47       60   783.333333  1000.000000  216.666667   
5      3        174      177  2900.000000  2950.000000   50.000000   
9      5        322      332  5366.666667  5533.333333  166.666667   

               column  
1  RElbow_speed_event  
5  RElbow_speed_event  
9  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        186      189  3100.000000  3150.000000      50.0   
3      2        265      268  4416.666667  4466.666667      50.0   

             column  
1  LHip_speed_event  
3  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1         82       99  1366.666667    1650.0  283.333333   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time    end_time  duration  \
1      1         41       50  683.333333  833.333333     150.0   

                           column  
1  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         43       46   716.666667   766.666667   50.000000   
3      2         64       83  1066.666667  1383.333333  316.666667   
7      4        308      309  5133.333333  5150.000000   16.666667   

                           column  
1  LWrist_vert_vel_movement_event  
3  LWrist_vert_vel_movement_event  
7  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 61-88 in LWrist_speed_event is completely within zero chunk 0-240 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 53-53 in RWrist_speed_event is completely within zero chunk 0-84 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 318-332 in RWrist_speed_event is completely within zero chunk 284-332 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_27_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
   value  start_idx  end_idx  start_time    end_time    duration  \
0      1          0       13         0.0  216.666667  216.666667   

              column  
0  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1          2       13    33.333333   216.666667  183.333333   
3      3         81       93  1350.000000  1550.000000  200.000000   
7      6        272      273  4533.333333  4550.000000   16.666667   
9      7        298      308  4966.666667  5133.333333  166.666667   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
7  RHeel_speed_event  
9  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         98      113  1633.333333  1883.333333  250.000000   
3      2        268      271  4466.666667  4516.666667   50.000000   
5      3        348      352  5800.000000  5866.666667   66.666667   

               column  
1  LElbow_speed_event  
3  LElbow_speed_event  
5  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time   duration  \
5      4        180      181      3000.0  3016.666667  16.666667   

                  column  
5  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
3      4        174      191  2900.000000  3183.333333  283.333333   
5      5        251      263  4183.333333  4383.333333  200.000000   
7      6        302      323  5033.333333  5383.333333  350.000000   

                  column  
3  LShoulder_speed_event  
5  LShoulder_speed_event  
7  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1        347      352  5783.333333  5866.666667  83.333333   

                           column  
1  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 49-98 in LWrist_speed_event is completely within zero chunk 0-352 in LWrist_vert_vel_movement_event
Last non-zero chunk 328-333 in LWrist_speed_event is completely within zero chunk 0-352 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_28_p1_annotated.csv
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
5      8        350      355  5833.333333  5916.666667  83.333333   

              column  
5  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         97      112  1616.666667  1866.666667     250.0   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time   duration  \
5      3        253      258  4216.666667    4300.0  83.333333   

             column  
5  Head_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration  \
3      4        204      219      3400.0    3650.0     250.0   

                  column  
3  LShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
3      2        205      224  3416.666667  3733.333333  316.666667   

                           column  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1        266      288  4433.333333    4800.0  366.666667   

                           column  
1  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 43-62 in LWrist_speed_event is completely within zero chunk 0-363 in LWrist_vert_vel_movement_event
Last non-zero chunk 264-286 in LWrist_speed_event is completely within zero chunk 0-363 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 45-144 in RWrist_speed_event partially overlaps with zero chunk 0-75 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 255-304 in RWrist_speed_event partially overlaps with zero chunk 301-363 in RWrist_vert_vel_movement_event
   value  start_idx  end_idx  start_time     end_time    duration  \
3      2        204      226      3400.0  3766.666667  366.666667   

               column  
3  RWrist_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
3      3        264      286      4400.0  4766.666667  366.666667   

               column  
3  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_29_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1          2       14    33.333333   233.333333     200.0   
5      6        226      244  3766.666667  4066.666667     300.0   

              column  
1  RHeel_speed_event  
5  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         62       67  1033.333333  1116.666667   83.333333   
3      2        130      136  2166.666667  2266.666667  100.000000   

               column  
1  LElbow_speed_event  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
5      4        255      262      4250.0  4366.666667  116.666667   

             column  
5  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         56       69   933.333333  1150.000000  216.666667   
5      4        245      262  4083.333333  4366.666667  283.333333   

                  column  
1  RShoulder_speed_event  
5  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
3      5        243      262      4050.0  4366.666667  316.666667   

                  column  
3  LShoulder_speed_event  
We need to turn fake events into 0s
No non-zero rows
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         54       64   900.000000  1066.666667  166.666667   
3      2        242      262  4033.333333  4366.666667  333.333333   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 46-66 in LWrist_speed_event is completely within zero chunk 0-262 in LWrist_vert_vel_movement_event
Last non-zero chunk 238-262 in LWrist_speed_event is completely within zero chunk 0-262 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 48-70 in RWrist_speed_event is completely within zero chunk 0-262 in RWrist_vert_vel_movement_event
Last non-zero chunk 237-262 in RWrist_speed_event is completely within zero chunk 0-262 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_2_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         25       27   416.666667   450.000000   33.333333   
3      2         97       98  1616.666667  1633.333333   16.666667   
5      3        192      193  3200.000000  3216.666667   16.666667   
9      8        330      344  5500.000000  5733.333333  233.333333   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
5  RHeel_speed_event  
9  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
5      5        335      335  5583.333333  5583.333333       0.0   

             column  
5  Head_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         42       58   700.000000   966.666667  266.666667   
7      5        401      403  6683.333333  6716.666667   33.333333   

                  column  
1  LShoulder_speed_event  
7  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 5-17 in LWrist_speed_event is completely within zero chunk 0-99 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap

Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 282-353 in RWrist_speed_event partially overlaps with zero chunk 348-403 in RWrist_vert_vel_movement_event
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time   duration  \
1      2         31       32  516.666667  533.333333  16.666667   

               column  
1  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_30_p1_annotated.csv
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        332      332  5533.333333  5533.333333       0.0   

              column  
1  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        173      189  2883.333333  3150.000000  266.666667   
3      3        232      233  3866.666667  3883.333333   16.666667   
5      4        284      284  4733.333333  4733.333333    0.000000   
7      5        310      311  5166.666667  5183.333333   16.666667   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
5  RHeel_speed_event  
7  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
5      4        316      332  5266.666667  5533.333333  266.666667   

               column  
5  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        271      280  4516.666667  4666.666667     150.0   

             column  
1  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time   duration  \
1      1         71       72  1183.333333    1200.0  16.666667   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1         71       73  1183.333333  1216.666667  33.333333   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         66       79      1100.0  1316.666667  216.666667   
3      2        321      332      5350.0  5533.333333  183.333333   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
5      4        329      332  5483.333333  5533.333333      50.0   

                           column  
5  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 58-83 in LWrist_speed_event is completely within zero chunk 0-164 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 313-332 in LWrist_speed_event is completely within zero chunk 282-332 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 61-84 in RWrist_speed_event is completely within zero chunk 0-332 in RWrist_vert_vel_movement_event
Last non-zero chunk 315-332 in RWrist_speed_event is completely within zero chunk 0-332 in RWrist_vert_vel_movement_event
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_31_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
   value  start_idx  end_idx   start_time     end_time  duration  \
3      2        433      433  7216.666667  7216.666667       0.0   

              column  
3  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time   duration  \
1      1        103      105  1716.666667    1750.0  33.333333   
5      6        240      240  4000.000000    4000.0   0.000000   

              column  
1  RHeel_speed_event  
5  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        177      193  2950.000000  3216.666667  266.666667   
3      2        344      365  5733.333333  6083.333333  350.000000   

               column  
1  LElbow_speed_event  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time   duration  \
3      3        348      353      5800.0  5883.333333  83.333333   

             column  
3  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         62       69  1033.333333  1150.000000  116.666667   
5      4        405      418  6750.000000  6966.666667  216.666667   

                  column  
1  RShoulder_speed_event  
5  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         62       68  1033.333333  1133.333333     100.0   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
We do not need to merge
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         60       73  1000.000000  1216.666667  216.666667   
3      2        410      425  6833.333333  7083.333333  250.000000   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         63       71  1050.000000  1183.333333  133.333333   
3      2        187      191  3116.666667  3183.333333   66.666667   

                           column  
1  LWrist_vert_vel_movement_event  
3  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 54-78 in LWrist_speed_event is completely within zero chunk 0-336 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 386-423 in LWrist_speed_event partially overlaps with zero chunk 414-433 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 54-77 in RWrist_speed_event is completely within zero chunk 0-433 in RWrist_vert_vel_movement_event
Last non-zero chunk 405-433 in RWrist_speed_event is completely within zero chunk 0-433 in RWrist_vert_vel_movement_event
   value  start_idx  end_idx  start_time     end_time    duration  \
1      2        180      193      3000.0  3216.666667  216.666667   

               column  
1  RWrist_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
1      2        184      192  3066.666667    3200.0  133.333333   

               column  
1  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_32_p1_annotated.csv
We need to merge
We need to merge
We do not need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1          2        9    33.333333   150.000000  116.666667   
3      2         89      104  1483.333333  1733.333333  250.000000   
5      4        147      148  2450.000000  2466.666667   16.666667   
9     11        279      300  4650.000000  5000.000000  350.000000   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
5  RHeel_speed_event  
9  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        108      112  1800.000000  1866.666667   66.666667   
3      2        142      154  2366.666667  2566.666667  200.000000   

               column  
1  LElbow_speed_event  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time   duration  \
1      1        157      162  2616.666667    2700.0  83.333333   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        104      112  1733.333333  1866.666667  133.333333   
3      2        294      300  4900.000000  5000.000000  100.000000   

             column  
1  LHip_speed_event  
3  LHip_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        127      131  2116.666667  2183.333333   66.666667   
3      2        152      159  2533.333333  2650.000000  116.666667   
5      3        298      300  4966.666667  5000.000000   33.333333   

              column  
1  RKnee_speed_event  
3  RKnee_speed_event  
5  RKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        187      188  3116.666667  3133.333333   16.666667   
3      2        244      265  4066.666667  4416.666667  350.000000   
5      3        283      300  4716.666667  5000.000000  283.333333   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
5  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        244      260  4066.666667  4333.333333  266.666667   

                           column  
1  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 59-78 in LWrist_speed_event is completely within zero chunk 0-300 in LWrist_vert_vel_movement_event
Last non-zero chunk 281-300 in LWrist_speed_event is completely within zero chunk 0-300 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 64-83 in RWrist_speed_event is completely within zero chunk 0-300 in RWrist_vert_vel_movement_event
Last non-zero chunk 186-300 in RWrist_speed_event is completely within zero chunk 0-300 in RWrist_vert_vel_movement_event
We need to merge
   value  start_idx  end_idx   start_time     end_time  duration  \
1      2        134      137  2233.333333  2283.333333      50.0   

               column  
1  RWrist_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      2        107      109  1783.333333  1816.666667   33.333333   
3      3        174      190  2900.000000  3166.666667  266.666667   

               column  
1  LWrist_speed_event  
3  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_33_p1_annotated.csv
We need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        210      220      3500.0  3666.666667  166.666667   

              column  
1  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time   duration  \
1      1         63       67      1050.0  1116.666667  66.666667   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1         66       75      1100.0    1250.0     150.0   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
5      3        163      169  2716.666667  2816.666667     100.0   

             column  
5  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1         63       66      1050.0    1100.0      50.0   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        211      220  3516.666667  3666.666667     150.0   

              column  
1  RKnee_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        194      209  3233.333333  3483.333333     250.0   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1        215      220  3583.333333  3666.666667  83.333333   

                           column  
1  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 53-77 in LWrist_speed_event is completely within zero chunk 0-220 in LWrist_vert_vel_movement_event
Last non-zero chunk 192-220 in LWrist_speed_event is completely within zero chunk 0-220 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 54-122 in RWrist_speed_event partially overlaps with zero chunk 0-59 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      2         99      109      1650.0  1816.666667  166.666667   

               column  
1  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_34_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We do not need to merge
No non-zero rows
No non-zero rows
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        232      242  3866.666667  4033.333333  166.666667   

              column  
1  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration  \
3      3        225      228      3750.0    3800.0      50.0   

              column  
3  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
3      2        223      242  3716.666667  4033.333333  316.666667   

               column  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
3      2        190      193  3166.666667  3216.666667      50.0   
5      3        239      242  3983.333333  4033.333333      50.0   

             column  
3  Head_speed_event  
5  Head_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1        223      224  3716.666667  3733.333333  16.666667   

             column  
1  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         65       83  1083.333333  1383.333333     300.0   
3      2        123      126  2050.000000  2100.000000      50.0   
5      3        224      242  3733.333333  4033.333333     300.0   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
5  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
No non-zero rows
   value  start_idx  end_idx  start_time   end_time   duration  \
0      1          0        1         0.0  16.666667  16.666667   

                           column  
0  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 63-120 in LWrist_speed_event is completely within zero chunk 0-242 in LWrist_vert_vel_movement_event
Last non-zero chunk 220-242 in LWrist_speed_event is completely within zero chunk 0-242 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 0-11 in RWrist_speed_event is completely within zero chunk 0-55 in RWrist_vert_vel_movement_event
no overlap
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_35_p1_annotated.csv
We need to merge
We do not need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         15       16   250.000000   266.666667   16.666667   
3      2        101      108  1683.333333  1800.000000  116.666667   
7      7        271      275  4516.666667  4583.333333   66.666667   
9      8        309      309  5150.000000  5150.000000    0.000000   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
7  RHeel_speed_event  
9  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
3      2        308      325  5133.333333  5416.666667  283.333333   

               column  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
3      2        147      153  2450.000000    2550.0  100.000000   
7      4        279      282  4650.000000    4700.0   50.000000   
9      5        322      327  5366.666667    5450.0   83.333333   

             column  
3  Head_speed_event  
7  Head_speed_event  
9  Head_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
3      4        201      215      3350.0  3583.333333  233.333333   

               column  
3  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time  end_time  duration            column
1      1        321      327      5350.0    5450.0     100.0  LHip_speed_event
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        267      277      4450.0  4616.666667  166.666667   

              column  
1  RKnee_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
3      2        172      172  2866.666667  2866.666667       0.0   

                  column  
3  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
We need to merge
   value  start_idx  end_idx   start_time     end_time    duration  \
5      4        226      233  3766.666667  3883.333333  116.666667   
7      5        281      304  4683.333333  5066.666667  383.333333   
9      6        327      327  5450.000000  5450.000000    0.000000   

                           column  
5  RWrist_vert_vel_movement_event  
7  RWrist_vert_vel_movement_event  
9  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        149      170  2483.333333  2833.333333  350.000000   
5      4        281      298  4683.333333  4966.666667  283.333333   
7      5        320      327  5333.333333  5450.000000  116.666667   

                           column  
1  LWrist_vert_vel_movement_event  
5  LWrist_vert_vel_movement_event  
7  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 70-94 in LWrist_speed_event is completely within zero chunk 0-197 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 279-327 in LWrist_speed_event is completely within zero chunk 247-327 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 74-119 in RWrist_speed_event partially overlaps with zero chunk 0-75 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 284-325 in RWrist_speed_event is completely within zero chunk 204-327 in RWrist_vert_vel_movement_event
We need to merge
   value  start_idx  end_idx  start_time     end_time    duration  \
3      5        228      236      3800.0  3933.333333  133.333333   

               column  
3  RWrist_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
1      2        108      115      1800.0  1916.666667  116.666667   

               column  
1  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_36_p1_annotated.csv
We do not need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1          9        9       150.0     150.0       0.0   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         73       86  1216.666667  1433.333333  216.666667   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         52       69   866.666667  1150.000000  283.333333   
3      2        117      131  1950.000000  2183.333333  233.333333   
5      3        179      184  2983.333333  3066.666667   83.333333   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
5  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time  end_time  duration  \
3      2        126      144      2100.0    2400.0     300.0   

               column  
3  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time   duration  \
1      1         60       65      1000.0  1083.333333  83.333333   

             column  
1  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1         51       69       850.0    1150.0     300.0   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
   value  start_idx  end_idx  start_time     end_time    duration  \
3      2        114      131      1900.0  2183.333333  283.333333   

                           column  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
3      2        233      256  3883.333333  4266.666667  383.333333   

                           column  
3  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 46-93 in LWrist_speed_event partially overlaps with zero chunk 0-65 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 235-256 in LWrist_speed_event is completely within zero chunk 95-256 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 47-99 in RWrist_speed_event partially overlaps with zero chunk 0-64 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_37_p1_annotated.csv
We do not need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         57       68       950.0  1133.333333  183.333333   

              column  
1  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         76       92  1266.666667  1533.333333  266.666667   
3      4        173      193  2883.333333  3216.666667  333.333333   
5      6        300      311  5000.000000  5183.333333  183.333333   
7      8        354      373  5900.000000  6216.666667  316.666667   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
5  RHeel_speed_event  
7  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        391      391  6516.666667  6516.666667       0.0   

             column  
1  RHip_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         74       77  1233.333333  1283.333333      50.0   

             column  
1  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
3      3        179      182  2983.333333  3033.333333      50.0   

                  column  
3  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        343      350  5716.666667  5833.333333  116.666667   
3      2        376      391  6266.666667  6516.666667  250.000000   

             column  
1  LHip_speed_event  
3  LHip_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time  end_time  duration  \
3      2        165      177      2750.0    2950.0     200.0   

              column  
3  RKnee_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
3      3        160      181  2666.666667  3016.666667  350.000000   
7      5        374      385  6233.333333  6416.666667  183.333333   

                  column  
3  LShoulder_speed_event  
7  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
We need to merge
   value  start_idx  end_idx   start_time  end_time   duration  \
5      5        307      309  5116.666667    5150.0  33.333333   

                           column  
5  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
5      5        295      301  4916.666667  5016.666667     100.0   

                           column  
5  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap

Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_38_p0_annotated.csv
We need to merge
We do not need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1         36       36       600.0     600.0       0.0   
7     13        423      429      7050.0    7150.0     100.0   

              column  
1  RHeel_speed_event  
7  RHeel_speed_event  
We need to turn fake events into 0s
    value  start_idx  end_idx   start_time     end_time    duration  \
7       7        342      356  5700.000000  5933.333333  233.333333   
9       8        392      399  6533.333333  6650.000000  116.666667   
11      9        419      434  6983.333333  7233.333333  250.000000   
13     10        456      469  7600.000000  7816.666667  216.666667   

                column  
7   LElbow_speed_event  
9   LElbow_speed_event  
11  LElbow_speed_event  
13  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1        343      347  5716.666667  5783.333333  66.666667   

             column  
1  Head_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
3      6        350      361  5833.333333  6016.666667  183.333333   

               column  
3  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         64       67  1066.666667  1116.666667      50.0   
7     13        446      449  7433.333333  7483.333333      50.0   

             column  
1  LHip_speed_event  
7  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
3      8        361      369  6016.666667    6150.0  133.333333   

                           column  
3  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 36-55 in LWrist_speed_event is completely within zero chunk 0-58 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 426-469 in LWrist_speed_event partially overlaps with zero chunk 460-469 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 35-293 in RWrist_speed_event partially overlaps with zero chunk 0-47 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 459-469 in RWrist_speed_event is completely within zero chunk 448-469 in RWrist_vert_vel_movement_event
We do not need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
3      6        343      358  5716.666667  5966.666667  250.000000   
5      7        394      407  6566.666667  6783.333333  216.666667   

               column  
3  LWrist_speed_event  
5  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_39_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
   value  start_idx  end_idx  start_time   end_time   duration  \
0      1          0        2         0.0  33.333333  33.333333   

              column  
0  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         86       95  1433.333333  1583.333333     150.0   

             column  
1  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time    end_time    duration  \
1      1          9       20       150.0  333.333333  183.333333   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
3      2        190      198  3166.666667    3300.0  133.333333   

              column  
3  RKnee_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1          9       22   150.000000   366.666667  216.666667   
7      5        373      386  6216.666667  6433.333333  216.666667   

                  column  
1  LShoulder_speed_event  
7  LShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
7      4        331      352  5516.666667  5866.666667     350.0   

                           column  
7  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 12-25 in LWrist_speed_event is completely within zero chunk 0-49 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 331-374 in LWrist_speed_event is completely within zero chunk 300-407 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 11-28 in RWrist_speed_event is completely within zero chunk 0-47 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 324-383 in RWrist_speed_event partially overlaps with zero chunk 372-407 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration  \
3      3         99      114      1650.0    1900.0     250.0   

               column  
3  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_3_p0_annotated.csv
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
4      3        143      153  2383.333333    2550.0  166.666667   

               column  
4  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1        146      153  2433.333333    2550.0  116.666667   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
2      3        142      153  2366.666667    2550.0  183.333333   

               column  
2  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time   duration  \
1      1        151      153  2516.666667    2550.0  33.333333   

              column  
1  RKnee_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1        146      153  2433.333333    2550.0  116.666667   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time   duration  \
2      3        104      108  1733.333333    1800.0  66.666667   

                           column  
2  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
no overlap
Last non-zero chunk 148-153 in LWrist_speed_event is completely within zero chunk 75-153 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
no overlap
Last non-zero chunk 145-153 in RWrist_speed_event is completely within zero chunk 104-153 in RWrist_vert_vel_movement_event
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_40_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
   value  start_idx  end_idx  start_time    end_time    duration  \
0      1          0       11         0.0  183.333333  183.333333   

              column  
0  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1        224      225  3733.333333  3750.000000  16.666667   
3      2        289      292  4816.666667  4866.666667  50.000000   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1          9        9   150.000000   150.000000       0.0   
9      6        287      290  4783.333333  4833.333333      50.0   

               column  
1  LElbow_speed_event  
9  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         21       34       350.0   566.666667  216.666667   
5      4        171      183      2850.0  3050.000000  200.000000   
7      5        204      212      3400.0  3533.333333  133.333333   
9      6        231      242      3850.0  4033.333333  183.333333   

               column  
1  RElbow_speed_event  
5  RElbow_speed_event  
7  RElbow_speed_event  
9  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
0      1          0        2     0.000000    33.333333   33.333333   
2      2        312      319  5200.000000  5316.666667  116.666667   
4      3        352      367  5866.666667  6116.666667  250.000000   

              column  
0  RKnee_speed_event  
2  RKnee_speed_event  
4  RKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We do not need to merge
We do not need to merge
   value  start_idx  end_idx  start_time     end_time    duration  \
0      1          0       20    0.000000   333.333333  333.333333   
2      2         56       80  933.333333  1333.333333  400.000000   

                           column  
0  RWrist_vert_vel_movement_event  
2  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time  duration  \
1      1          6       24  100.000000   400.000000     300.0   
3      2         59       83  983.333333  1383.333333     400.0   

                           column  
1  LWrist_vert_vel_movement_event  
3  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 10-24 in LWrist_speed_event is completely within zero chunk 0-155 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 323-367 in LWrist_speed_event partially overlaps with zero chunk 364-367 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 0-13 in RWrist_speed_event is completely within zero chunk 0-156 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 324-367 in RWrist_speed_event partially overlaps with zero chunk 358-367 in RWrist_vert_vel_movement_event
We need to merge
We do not need to merge
   value  start_idx  end_idx   start_time     end_time    duration  \
1      2         23       32   383.333333   533.333333  150.000000   
3      3         94      108  1566.666667  1800.000000  233.333333   
7      6        230      251  3833.333333  4183.333333  350.000000   

               column  
1  RWrist_speed_event  
3  RWrist_speed_event  
7  RWrist_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      2         66       79  1100.000000  1316.666667  216.666667   
3      3        170      190  2833.333333  3166.666667  333.333333   

               column  
1  LWrist_speed_event  
3  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_41_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
   value  start_idx  end_idx   start_time  end_time    duration  \
3      3        274      291  4566.666667    4850.0  283.333333   

              column  
3  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        166      176  2766.666667  2933.333333  166.666667   
3      2        217      220  3616.666667  3666.666667   50.000000   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time   duration  \
3      4        286      291  4766.666667    4850.0  83.333333   

             column  
3  Head_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time   duration  \
1      1        153      157      2550.0  2616.666667  66.666667   

             column  
1  LHip_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time   duration  \
5      3        287      291  4783.333333    4850.0  66.666667   

              column  
5  RKnee_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         71       81  1183.333333  1350.000000  166.666667   
3      3        128      136  2133.333333  2266.666667  133.333333   
5      4        167      188  2783.333333  3133.333333  350.000000   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
5  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 33-284 in LWrist_speed_event partially overlaps with zero chunk 0-54 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 33-284 in LWrist_speed_event partially overlaps with zero chunk 280-291 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 36-45 in RWrist_speed_event is completely within zero chunk 0-54 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 49-287 in RWrist_speed_event partially overlaps with zero chunk 275-291 in RWrist_vert_vel_movement_event
We do not need to merge
We do not need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_43_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1         29       32   483.333333   533.333333  50.000000   
5      4        227      232  3783.333333  3866.666667  83.333333   

                  column  
1  RShoulder_speed_event  
5  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         26       32   433.333333   533.333333     100.0   
3      2         72       78  1200.000000  1300.000000     100.0   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 28-41 in LWrist_speed_event is completely within zero chunk 0-126 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 237-341 in LWrist_speed_event partially overlaps with zero chunk 310-352 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 27-39 in RWrist_speed_event is completely within zero chunk 0-59 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 349-352 in RWrist_speed_event is completely within zero chunk 315-352 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_44_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time  duration  \
1      1         37       52  616.666667  866.666667     250.0   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time   duration  \
1      1        172      174  2866.666667    2900.0  33.333333   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time  end_time    duration  \
1      1         35       45  583.333333     750.0  166.666667   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         69       87  1150.000000  1450.000000  300.000000   
3      2        152      160  2533.333333  2666.666667  133.333333   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
We need to merge
   value  start_idx  end_idx   start_time     end_time    duration  \
3      3        227      241  3783.333333  4016.666667  233.333333   
5      4        260      275  4333.333333  4583.333333  250.000000   

                           column  
3  RWrist_vert_vel_movement_event  
5  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 36-53 in LWrist_speed_event is completely within zero chunk 0-70 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 335-341 in LWrist_speed_event is completely within zero chunk 216-364 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 34-48 in RWrist_speed_event is completely within zero chunk 0-72 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 227-350 in RWrist_speed_event is completely within zero chunk 193-364 in RWrist_vert_vel_movement_event
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
3      5        265      276  4416.666667    4600.0  183.333333   

               column  
3  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_45_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        340      340  5666.666667  5666.666667       0.0   

              column  
1  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time   duration  \
1      1        234      235      3900.0  3916.666667  16.666667   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
3      3        325      333  5416.666667    5550.0  133.333333   

               column  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
3      3        191      203  3183.333333  3383.333333     200.0   

                  column  
3  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1        137      139  2283.333333  2316.666667  33.333333   

             column  
1  LHip_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
5      3        278      291  4633.333333    4850.0  216.666667   

              column  
5  RKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
3      2        140      160  2333.333333  2666.666667  333.333333   
5      3        217      235  3616.666667  3916.666667  300.000000   

                           column  
3  LWrist_vert_vel_movement_event  
5  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 33-178 in LWrist_speed_event partially overlaps with zero chunk 0-56 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 316-340 in LWrist_speed_event is completely within zero chunk 97-340 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 31-47 in RWrist_speed_event is completely within zero chunk 0-57 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 333-340 in RWrist_speed_event is completely within zero chunk 320-340 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
3      2        220      241  3666.666667  4016.666667     350.0   

               column  
3  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_46_p0_annotated.csv
We need to merge
We do not need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        420      437      7000.0  7283.333333  283.333333   

              column  
1  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1         93       95  1550.000000  1583.333333  33.333333   
3      2        197      200  3283.333333  3333.333333  50.000000   
5      3        243      244  4050.000000  4066.666667  16.666667   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
5  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        437      437  7283.333333  7283.333333       0.0   

             column  
1  RHip_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time   duration  \
3      2        345      349      5750.0  5816.666667  66.666667   

             column  
3  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         71       92  1183.333333  1533.333333     350.0   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        266      273  4433.333333  4550.000000  116.666667   
3      2        427      437  7116.666667  7283.333333  166.666667   

             column  
1  LHip_speed_event  
3  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         86       95  1433.333333  1583.333333  150.000000   
5      4        288      303  4800.000000  5050.000000  250.000000   
7      5        418      437  6966.666667  7283.333333  316.666667   

                  column  
1  LShoulder_speed_event  
5  LShoulder_speed_event  
7  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 8-19 in LWrist_speed_event is completely within zero chunk 0-78 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 5-20 in RWrist_speed_event is completely within zero chunk 0-79 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_47_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         36       49       600.0   816.666667  216.666667   
3      2        222      225      3700.0  3750.000000   50.000000   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time  end_time  duration  \
3      2        204      225      3400.0    3750.0     350.0   

               column  
3  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time    duration  \
1      1         36       49       600.0  816.666667  216.666667   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 37-53 in LWrist_speed_event is completely within zero chunk 0-225 in LWrist_vert_vel_movement_event
Last non-zero chunk 219-225 in LWrist_speed_event is completely within zero chunk 0-225 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 39-87 in RWrist_speed_event partially overlaps with zero chunk 0-55 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_48_p0_annotated.csv
We do not need to merge
We do not need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time   duration  \
1      1         51       52       850.0  866.666667  16.666667   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
5      3        304      312  5066.666667    5200.0  133.333333   

               column  
5  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1        303      306      5050.0    5100.0      50.0   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
3      3        124      135  2066.666667  2250.000000  183.333333   
5      4        169      187  2816.666667  3116.666667  300.000000   
7      5        208      223  3466.666667  3716.666667  250.000000   

               column  
3  RElbow_speed_event  
5  RElbow_speed_event  
7  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time    duration  \
1      1         40       51  666.666667     850.0  183.333333   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
5      3        289      298  4816.666667  4966.666667  150.000000   
7      4        319      323  5316.666667  5383.333333   66.666667   

                           column  
5  LWrist_vert_vel_movement_event  
7  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 38-51 in LWrist_speed_event is completely within zero chunk 0-152 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 289-307 in LWrist_speed_event is completely within zero chunk 259-323 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 37-89 in RWrist_speed_event partially overlaps with zero chunk 0-54 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
We need to merge
   value  start_idx  end_idx   start_time  end_time    duration  \
3      2        127      135  2116.666667    2250.0  133.333333   
5      3        173      183  2883.333333    3050.0  166.666667   
7      4        214      216  3566.666667    3600.0   33.333333   

               column  
3  RWrist_speed_event  
5  RWrist_speed_event  
7  RWrist_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_49_p0_annotated.csv
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time  duration  \
1      1         34       49  566.666667  816.666667     250.0   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1        196      204  3266.666667    3400.0  133.333333   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time    duration  \
1      1         42       49       700.0  816.666667  116.666667   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1        102      111      1700.0    1850.0     150.0   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
We do not need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 38-49 in LWrist_speed_event is completely within zero chunk 0-204 in LWrist_vert_vel_movement_event
Last non-zero chunk 184-192 in LWrist_speed_event is completely within zero chunk 0-204 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 37-83 in RWrist_speed_event partially overlaps with zero chunk 0-47 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_4_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
    value  start_idx  end_idx   start_time     end_time    duration  \
7       6        480      486  8000.000000  8100.000000  100.000000   
9       7        517      529  8616.666667  8816.666667  200.000000   
11      8        557      570  9283.333333  9500.000000  216.666667   

                column  
7   LElbow_speed_event  
9   LElbow_speed_event  
11  LElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1        380      387  6333.333333    6450.0  116.666667   

             column  
1  RHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         30       37       500.0   616.666667  116.666667   
5      4        558      568      9300.0  9466.666667  166.666667   

                  column  
1  RShoulder_speed_event  
5  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1         26       33   433.333333     550.0  116.666667   
7      8        555      564  9250.000000    9400.0  150.000000   

               column  
1  RElbow_speed_event  
7  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time    end_time  duration  \
1      1         34       34  566.666667  566.666667       0.0   

             column  
1  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
    value  start_idx  end_idx   start_time     end_time    duration  \
1       1         28       40   466.666667   666.666667  200.000000   
3       2        171      178  2850.000000  2966.666667  116.666667   
5       3        220      235  3666.666667  3916.666667  250.000000   
7       4        269      277  4483.333333  4616.666667  133.333333   
11      7        558      566  9300.000000  9433.333333  133.333333   

                   column  
1   LShoulder_speed_event  
3   LShoulder_speed_event  
5   LShoulder_speed_event  
7   LShoulder_speed_event  
11  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 29-42 in LWrist_speed_event is completely within zero chunk 0-182 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 542-567 in LWrist_speed_event is completely within zero chunk 529-600 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 26-45 in RWrist_speed_event is completely within zero chunk 0-186 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 548-568 in RWrist_speed_event is completely within zero chunk 529-600 in RWrist_vert_vel_movement_event
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_50_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         63       70  1050.000000  1166.666667  116.666667   
3      2        160      164  2666.666667  2733.333333   66.666667   

              column  
1  LKnee_speed_event  
3  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
3      2        140      153  2333.333333    2550.0  216.666667   

               column  
3  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        163      170  2716.666667  2833.333333  116.666667   
3      2        233      243  3883.333333  4050.000000  166.666667   

             column  
1  LHip_speed_event  
3  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 32-45 in LWrist_speed_event is completely within zero chunk 0-332 in LWrist_vert_vel_movement_event
Last non-zero chunk 32-45 in LWrist_speed_event is completely within zero chunk 0-332 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 30-121 in RWrist_speed_event partially overlaps with zero chunk 0-42 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_51_p0_annotated.csv
We need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
5      6        223      223  3716.666667  3716.666667       0.0   

              column  
5  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        131      137  2183.333333  2283.333333     100.0   
3      2        283      298  4716.666667  4966.666667     250.0   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1         27       33       450.0     550.0     100.0   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         82       92  1366.666667  1533.333333  166.666667   
3      2        123      144  2050.000000  2400.000000  350.000000   
5      3        175      183  2916.666667  3050.000000  133.333333   
7      4        292      295  4866.666667  4916.666667   50.000000   
9      5        384      392  6400.000000  6533.333333  133.333333   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
5  LShoulder_speed_event  
7  LShoulder_speed_event  
9  LShoulder_speed_event  
We need to turn fake events into 0s
We do not need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 28-40 in LWrist_speed_event is completely within zero chunk 0-59 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 386-393 in LWrist_speed_event is completely within zero chunk 306-426 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 24-40 in RWrist_speed_event is completely within zero chunk 0-60 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We do not need to merge
   value  start_idx  end_idx  start_time     end_time    duration  \
3      3        249      256      4150.0  4266.666667  116.666667   

               column  
3  RWrist_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time  end_time  duration  \
5      4        333      333      5550.0    5550.0       0.0   

               column  
5  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_52_p0_annotated.csv
We need to merge
We need to merge
We do not need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        444      452      7400.0  7533.333333  133.333333   

              column  
1  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1         29       30   483.333333   500.000000  16.666667   
3      2        198      199  3300.000000  3316.666667  16.666667   
5      3        372      376  6200.000000  6266.666667  66.666667   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
5  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        151      162  2516.666667  2700.000000  183.333333   
5      3        286      290  4766.666667  4833.333333   66.666667   

               column  
1  LElbow_speed_event  
5  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration            column
3      3        162      165      2700.0    2750.0      50.0  Head_speed_event
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time    end_time    duration  \
1      1         27       37       450.0  616.666667  166.666667   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         28       33   466.666667   550.000000   83.333333   
5      5        316      332  5266.666667  5533.333333  266.666667   

               column  
1  RElbow_speed_event  
5  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        340      357  5666.666667  5950.000000  283.333333   
3      2        438      452  7300.000000  7533.333333  233.333333   

              column  
1  RKnee_speed_event  
3  RKnee_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         29       34   483.333333   566.666667   83.333333   
3      2        151      163  2516.666667  2716.666667  200.000000   
5      3        221      235  3683.333333  3916.666667  233.333333   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
5  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
We do not need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 29-47 in LWrist_speed_event is completely within zero chunk 0-221 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 405-445 in LWrist_speed_event partially overlaps with zero chunk 443-452 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 31-41 in RWrist_speed_event is completely within zero chunk 0-79 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
We do not need to merge
   value  start_idx  end_idx   start_time     end_time  duration  \
5      5        320      329  5333.333333  5483.333333     150.0   

               column  
5  RWrist_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_53_p1_annotated.csv
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time   duration  \
1      1        231      232      3850.0  3866.666667  16.666667   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        284      293  4733.333333  4883.333333     150.0   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1         55       69   916.666667    1150.0  233.333333   
5      4        374      387  6233.333333    6450.0  216.666667   

                  column  
1  RShoulder_speed_event  
5  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
3      3        367      383  6116.666667  6383.333333  266.666667   

               column  
3  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         57       68   950.000000  1133.333333  183.333333   
5      4        382      382  6366.666667  6366.666667    0.000000   

                  column  
1  LShoulder_speed_event  
5  LShoulder_speed_event  
We need to turn fake events into 0s
We do not need to merge
No non-zero rows
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         57       71       950.0  1183.333333  233.333333   

                           column  
1  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 49-73 in LWrist_speed_event is completely within zero chunk 0-405 in LWrist_vert_vel_movement_event
Last non-zero chunk 376-398 in LWrist_speed_event is completely within zero chunk 0-405 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 51-74 in RWrist_speed_event is completely within zero chunk 0-261 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We do not need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
1      2        281      300  4683.333333    5000.0  316.666667   

               column  
1  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_54_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        431      445  7183.333333  7416.666667  233.333333   

              column  
1  LHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         96      106  1600.000000  1766.666667  166.666667   
3      3        163      163  2716.666667  2716.666667    0.000000   
5      4        241      257  4016.666667  4283.333333  266.666667   
7      6        338      347  5633.333333  5783.333333  150.000000   
9      7        386      396  6433.333333  6600.000000  166.666667   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
5  RHeel_speed_event  
7  RHeel_speed_event  
9  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        132      144  2200.000000  2400.000000  200.000000   
3      2        196      215  3266.666667  3583.333333  316.666667   
5      3        335      354  5583.333333  5900.000000  316.666667   
9      5        441      445  7350.000000  7416.666667   66.666667   

               column  
1  LElbow_speed_event  
3  LElbow_speed_event  
5  LElbow_speed_event  
9  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time  duration  \
1      1         46       55  766.666667  916.666667     150.0   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time   duration  \
1      1        356      357  5933.333333    5950.0  16.666667   

             column  
1  LHip_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        374      385  6233.333333  6416.666667  183.333333   

              column  
1  RKnee_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time    end_time  duration  \
1      1         47       56  783.333333  933.333333     150.0   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
   value  start_idx  end_idx  start_time    end_time    duration  \
1      1         45       56       750.0  933.333333  183.333333   

                           column  
1  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
5      3        395      403  6583.333333  6716.666667  133.333333   

                           column  
5  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 37-58 in LWrist_speed_event is completely within zero chunk 0-124 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 384-421 in LWrist_speed_event is completely within zero chunk 217-445 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 39-60 in RWrist_speed_event is completely within zero chunk 0-199 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 380-425 in RWrist_speed_event partially overlaps with zero chunk 422-445 in RWrist_vert_vel_movement_event
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_55_p1_annotated.csv
We need to merge
We do not need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time   duration  \
1      1        255      259      4250.0  4316.666667  66.666667   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
3      3        499      499  8316.666667  8316.666667       0.0   

             column  
3  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
3      3        406      413  6766.666667  6883.333333  116.666667   
5      4        444      452  7400.000000  7533.333333  133.333333   

                  column  
3  RShoulder_speed_event  
5  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1        103      111  1716.666667    1850.0  133.333333   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        261      268  4350.000000  4466.666667  116.666667   
3      2        309      309  5150.000000  5150.000000    0.000000   
5      3        409      412  6816.666667  6866.666667   50.000000   

             column  
1  LHip_speed_event  
3  LHip_speed_event  
5  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration  \
3      4        402      417      6700.0    6950.0     250.0   

                  column  
3  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         93      110  1550.000000  1833.333333  283.333333   
7      4        409      427  6816.666667  7116.666667  300.000000   

                           column  
1  RWrist_vert_vel_movement_event  
7  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1         98      100  1633.333333  1666.666667  33.333333   

                           column  
1  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 86-112 in LWrist_speed_event is completely within zero chunk 0-321 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 467-494 in LWrist_speed_event is completely within zero chunk 428-499 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 88-115 in RWrist_speed_event is completely within zero chunk 0-145 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 468-498 in RWrist_speed_event is completely within zero chunk 324-499 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      2        253      274  4216.666667  4566.666667     350.0   

               column  
1  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_56_p1_annotated.csv
We need to merge
We do not need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         74       78  1233.333333  1300.000000   66.666667   
3      2        115      121  1916.666667  2016.666667  100.000000   
5      4        202      210  3366.666667  3500.000000  133.333333   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
5  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        132      140  2200.000000  2333.333333  133.333333   
3      2        203      210  3383.333333  3500.000000  116.666667   

             column  
1  Head_speed_event  
3  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         72       74  1200.000000  1233.333333   33.333333   
3      2        124      137  2066.666667  2283.333333  216.666667   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1         71       78  1183.333333    1300.0  116.666667   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 16-52 in LWrist_speed_event partially overlaps with zero chunk 0-18 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 183-210 in LWrist_speed_event is completely within zero chunk 70-210 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 17-51 in RWrist_speed_event partially overlaps with zero chunk 0-39 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_57_p1_annotated.csv
We do not need to merge
We do not need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        169      169  2816.666667  2816.666667       0.0   
3      2        225      225  3750.000000  3750.000000       0.0   
5      3        290      296  4833.333333  4933.333333     100.0   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
5  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        148      166  2466.666667  2766.666667     300.0   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
3      2        230      238  3833.333333  3966.666667  133.333333   

                  column  
3  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         81       88      1350.0  1466.666667  116.666667   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
3      3        223      238  3716.666667  3966.666667     250.0   

                  column  
3  LShoulder_speed_event  
We need to turn fake events into 0s
We do not need to merge
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         66       83      1100.0  1383.333333  283.333333   

                           column  
1  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 22-23 in LWrist_speed_event is completely within zero chunk 0-132 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 278-314 in LWrist_speed_event partially overlaps with zero chunk 309-316 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 21-43 in RWrist_speed_event is completely within zero chunk 0-130 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
We do not need to merge
   value  start_idx  end_idx  start_time  end_time  duration  \
1      2         63       87      1050.0    1450.0     400.0   

               column  
1  RWrist_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time    end_time    duration  \
1      2         34       44  566.666667  733.333333  166.666667   

               column  
1  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_58_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1         97       98  1616.666667  1633.333333  16.666667   

              column  
1  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         20       23   333.333333   383.333333   50.000000   
3      2        161      183  2683.333333  3050.000000  366.666667   
5      4        274      279  4566.666667  4650.000000   83.333333   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
5  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
3      3        200      205  3333.333333  3416.666667   83.333333   
5      4        234      251  3900.000000  4183.333333  283.333333   

               column  
3  LElbow_speed_event  
5  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         59       67  983.333333  1116.666667  133.333333   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1        163      167  2716.666667  2783.333333  66.666667   
3      2        240      245  4000.000000  4083.333333  83.333333   

             column  
1  LHip_speed_event  
3  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
0      1          0        9         0.0   150.000000  150.000000   
4      8        309      320      5150.0  5333.333333  183.333333   

                  column  
0  LShoulder_speed_event  
4  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         55       62   916.666667  1033.333333  116.666667   
3      2        152      158  2533.333333  2633.333333  100.000000   

                           column  
1  LWrist_vert_vel_movement_event  
3  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 51-72 in LWrist_speed_event is completely within zero chunk 0-320 in LWrist_vert_vel_movement_event
Last non-zero chunk 147-165 in LWrist_speed_event is completely within zero chunk 0-320 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      2         80      101  1333.333333  1683.333333     350.0   
3      3        147      165  2450.000000  2750.000000     300.0   

               column  
1  LWrist_speed_event  
3  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_59_p1_annotated.csv
We need to merge
We do not need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1        306      321      5100.0    5350.0     250.0   

              column  
1  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1        128      130  2133.333333  2166.666667  33.333333   
5      6        250      251  4166.666667  4183.333333  16.666667   

              column  
1  RHeel_speed_event  
5  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time    end_time  duration  \
1      1         46       46  766.666667  766.666667       0.0   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         53       59   883.333333   983.333333  100.000000   
3      2        282      289  4700.000000  4816.666667  116.666667   

             column  
1  Head_speed_event  
3  Head_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         41       52   683.333333   866.666667  183.333333   
7      6        298      305  4966.666667  5083.333333  116.666667   

               column  
1  RElbow_speed_event  
7  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
5      6        286      302  4766.666667  5033.333333  266.666667   

                           column  
5  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 38-59 in LWrist_speed_event is completely within zero chunk 0-68 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 266-301 in LWrist_speed_event is completely within zero chunk 248-321 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 41-59 in RWrist_speed_event is completely within zero chunk 0-68 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      2         68       89  1133.333333  1483.333333     350.0   

               column  
1  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_5_p0_annotated.csv
We do not need to merge
We do not need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time    duration  \
1      1          9        9  150.000000     150.0    0.000000   
3      2         56       75  933.333333    1250.0  316.666667   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time  duration  \
1      1         41       47  683.333333  783.333333     100.0   

             column  
1  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
3      3        106      123  1766.666667    2050.0  283.333333   

                  column  
3  LShoulder_speed_event  
We need to turn fake events into 0s
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 12-32 in LWrist_speed_event is completely within zero chunk 0-179 in LWrist_vert_vel_movement_event
Last non-zero chunk 95-128 in LWrist_speed_event is completely within zero chunk 0-179 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 8-73 in RWrist_speed_event partially overlaps with zero chunk 0-28 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      2         55       65  916.666667  1083.333333  166.666667   

               column  
1  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_60_p1_annotated.csv
We do not need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        118      130  1966.666667  2166.666667  200.000000   
3      2        174      193  2900.000000  3216.666667  316.666667   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         74       91  1233.333333  1516.666667  283.333333   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
3      2        133      148  2216.666667  2466.666667     250.0   

                           column  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
0      1          0        2    0.000000    33.333333   33.333333   
2      2         58       77  966.666667  1283.333333  316.666667   

                           column  
0  LWrist_vert_vel_movement_event  
2  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 0-1 in LWrist_speed_event is completely within zero chunk 0-205 in LWrist_vert_vel_movement_event
Last non-zero chunk 61-75 in LWrist_speed_event is completely within zero chunk 0-205 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 0-3 in RWrist_speed_event is completely within zero chunk 0-57 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
   value  start_idx  end_idx   start_time     end_time    duration  \
3      3        128      151  2133.333333  2516.666667  383.333333   

               column  
3  RWrist_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
1      2         61       75  1016.666667    1250.0  233.333333   

               column  
1  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_61_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         16       21   266.666667   350.000000   83.333333   
3      2         81       98  1350.000000  1633.333333  283.333333   
5      4        170      183  2833.333333  3050.000000  216.666667   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
5  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time  end_time    duration  \
1      1         52       69  866.666667    1150.0  283.333333   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration  \
0      1          0        3         0.0      50.0      50.0   

               column  
0  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We do not need to merge
   value  start_idx  end_idx   start_time     end_time    duration  \
0      1          0       15     0.000000   250.000000  250.000000   
2      2         53       74   883.333333  1233.333333  350.000000   
4      3        182      183  3033.333333  3050.000000   16.666667   

                           column  
0  RWrist_vert_vel_movement_event  
2  RWrist_vert_vel_movement_event  
4  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1          2       13    33.333333   216.666667  183.333333   
3      2         52       75   866.666667  1250.000000  383.333333   
5      3        171      183  2850.000000  3050.000000  200.000000   

                           column  
1  LWrist_vert_vel_movement_event  
3  LWrist_vert_vel_movement_event  
5  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 0-15 in LWrist_speed_event is completely within zero chunk 0-183 in LWrist_vert_vel_movement_event
Last non-zero chunk 160-183 in LWrist_speed_event is completely within zero chunk 0-183 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 0-16 in RWrist_speed_event is completely within zero chunk 0-183 in RWrist_vert_vel_movement_event
Last non-zero chunk 163-183 in RWrist_speed_event is completely within zero chunk 0-183 in RWrist_vert_vel_movement_event
   value  start_idx  end_idx  start_time  end_time    duration  \
1      2         52       75  866.666667    1250.0  383.333333   

               column  
1  RWrist_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
3      3        120      127      2000.0  2116.666667  116.666667   

               column  
3  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_62_p1_annotated.csv
We need to merge
We need to merge
No non-zero rows
No non-zero rows
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        320      323  5333.333333  5383.333333      50.0   

              column  
1  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time   duration  \
1      1         15       16       250.0  266.666667  16.666667   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        311      323  5183.333333  5383.333333     200.0   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        185      193  3083.333333  3216.666667  133.333333   

             column  
1  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         62       75  1033.333333  1250.000000  216.666667   
3      2        226      244  3766.666667  4066.666667  300.000000   
5      3        311      323  5183.333333  5383.333333  200.000000   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
5  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time   duration  \
3      2        139      144  2316.666667    2400.0  83.333333   

               column  
3  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        317      323  5283.333333  5383.333333     100.0   

             column  
1  LHip_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        323      323  5383.333333  5383.333333       0.0   

              column  
1  RKnee_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         61       69  1016.666667  1150.000000  133.333333   
3      2        310      323  5166.666667  5383.333333  216.666667   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         59       64   983.333333  1066.666667   83.333333   
3      2        313      323  5216.666667  5383.333333  166.666667   

                           column  
1  LWrist_vert_vel_movement_event  
3  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 57-77 in LWrist_speed_event is completely within zero chunk 0-323 in LWrist_vert_vel_movement_event
Last non-zero chunk 302-323 in LWrist_speed_event is completely within zero chunk 0-323 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_63_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        426      436      7100.0  7266.666667  166.666667   

              column  
1  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1        140      141  2333.333333  2350.000000  16.666667   
3      2        286      287  4766.666667  4783.333333  16.666667   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
3      2        232      251  3866.666667  4183.333333  316.666667   
5      3        328      333  5466.666667  5550.000000   83.333333   
9      5        432      436  7200.000000  7266.666667   66.666667   

               column  
3  LElbow_speed_event  
5  LElbow_speed_event  
9  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         73       80  1216.666667  1333.333333  116.666667   
3      2        186      190  3100.000000  3166.666667   66.666667   

             column  
1  Head_speed_event  
3  Head_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
3      2        154      173  2566.666667  2883.333333  316.666667   

               column  
3  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        430      436  7166.666667  7266.666667     100.0   

             column  
1  LHip_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        422      436  7033.333333  7266.666667  233.333333   

              column  
1  RKnee_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        129      143  2150.000000  2383.333333  233.333333   
3      2        191      192  3183.333333  3200.000000   16.666667   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1         96      120      1600.0    2000.0     400.0   

                           column  
1  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
0      1          0        3     0.000000    50.000000   50.000000   
4      3        170      174  2833.333333  2900.000000   66.666667   
6      4        378      397  6300.000000  6616.666667  316.666667   
8      5        431      436  7183.333333  7266.666667   83.333333   

                           column  
0  LWrist_vert_vel_movement_event  
4  LWrist_vert_vel_movement_event  
6  LWrist_vert_vel_movement_event  
8  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 0-5 in LWrist_speed_event is completely within zero chunk 0-98 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 423-436 in LWrist_speed_event is completely within zero chunk 127-436 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 0-10 in RWrist_speed_event is completely within zero chunk 0-213 in RWrist_vert_vel_movement_event
no overlap
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
3      4        329      337  5483.333333  5616.666667  133.333333   

               column  
3  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_64_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
    value  start_idx  end_idx   start_time     end_time    duration  \
1       1          9        9   150.000000   150.000000    0.000000   
3       2        157      180  2616.666667  3000.000000  383.333333   
7       7        369      373  6150.000000  6216.666667   66.666667   
11     11        455      461  7583.333333  7683.333333  100.000000   

               column  
1   RHeel_speed_event  
3   RHeel_speed_event  
7   RHeel_speed_event  
11  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        143      160  2383.333333  2666.666667  283.333333   
3      3        195      208  3250.000000  3466.666667  216.666667   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1         73       74  1216.666667  1233.333333  16.666667   
5      4        296      298  4933.333333  4966.666667  33.333333   

               column  
1  RElbow_speed_event  
5  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         59       67   983.333333  1116.666667  133.333333   
3      2        151      156  2516.666667  2600.000000   83.333333   
5      3        218      218  3633.333333  3633.333333    0.000000   
7      4        292      295  4866.666667  4916.666667   50.000000   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
5  LShoulder_speed_event  
7  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
We need to merge
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         58       71   966.666667  1183.333333  216.666667   
7      5        432      447  7200.000000  7450.000000  250.000000   

                           column  
1  RWrist_vert_vel_movement_event  
7  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 48-75 in LWrist_speed_event is completely within zero chunk 0-169 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 427-446 in LWrist_speed_event is completely within zero chunk 238-461 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 51-74 in RWrist_speed_event is completely within zero chunk 0-146 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 424-449 in RWrist_speed_event is completely within zero chunk 414-461 in RWrist_vert_vel_movement_event
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_65_p1_annotated.csv
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         69       69  1150.000000  1150.000000       0.0   
3      2         97      100  1616.666667  1666.666667      50.0   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
3      2        146      157  2433.333333  2616.666667  183.333333   

               column  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap

Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_67_p0_annotated.csv
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
   value  start_idx  end_idx  start_time     end_time   duration  \
1      1        204      205      3400.0  3416.666667  16.666667   

              column  
1  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time   duration  \
1      1        132      133      2200.0  2216.666667  16.666667   
3      2        162      164      2700.0  2733.333333  33.333333   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         89      109  1483.333333  1816.666667  333.333333   
3      2        197      205  3283.333333  3416.666667  133.333333   

               column  
1  LElbow_speed_event  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
3      4        194      205  3233.333333  3416.666667  183.333333   

                  column  
3  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         51       68   850.000000  1133.333333  283.333333   
3      2        190      205  3166.666667  3416.666667  250.000000   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1        203      205  3383.333333  3416.666667  33.333333   

              column  
1  RKnee_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
3      3        195      205      3250.0  3416.666667  166.666667   

                  column  
3  LShoulder_speed_event  
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 42-69 in LWrist_speed_event is completely within zero chunk 0-205 in LWrist_vert_vel_movement_event
Last non-zero chunk 185-205 in LWrist_speed_event is completely within zero chunk 0-205 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 44-73 in RWrist_speed_event is completely within zero chunk 0-205 in RWrist_vert_vel_movement_event
Last non-zero chunk 176-205 in RWrist_speed_event is completely within zero chunk 0-205 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_68_p0_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         62       79  1033.333333  1316.666667  283.333333   
3      2        191      204  3183.333333  3400.000000  216.666667   
5      3        236      245  3933.333333  4083.333333  150.000000   

               column  
1  LElbow_speed_event  
3  LElbow_speed_event  
5  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1        177      198      2950.0    3300.0     350.0   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time   duration  \
1      1        240      242      4000.0  4033.333333  33.333333   

             column  
1  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time   duration  \
3      3        295      297  4916.666667    4950.0  33.333333   

                  column  
3  LShoulder_speed_event  
We need to turn fake events into 0s
We do not need to merge
We do not need to merge
   value  start_idx  end_idx  start_time     end_time    duration  \
5      3        273      287      4550.0  4783.333333  233.333333   

                           column  
5  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1         68       90  1133.333333    1500.0  366.666667   
3      2        273      291  4550.000000    4850.0  300.000000   

                           column  
1  LWrist_vert_vel_movement_event  
3  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 46-90 in LWrist_speed_event is completely within zero chunk 0-297 in LWrist_vert_vel_movement_event
Last non-zero chunk 263-297 in LWrist_speed_event is completely within zero chunk 0-297 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 50-70 in RWrist_speed_event is completely within zero chunk 0-164 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 266-296 in RWrist_speed_event is completely within zero chunk 256-297 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_69_p0_annotated.csv
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        143      151  2383.333333  2516.666667  133.333333   
3      2        186      198  3100.000000  3300.000000  200.000000   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         27       39       450.0   650.000000  200.000000   
5      4        327      338      5450.0  5633.333333  183.333333   

                  column  
1  RShoulder_speed_event  
5  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         46       50   766.666667   833.333333   66.666667   
3      2        106      112  1766.666667  1866.666667  100.000000   
5      3        163      170  2716.666667  2833.333333  116.666667   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
5  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        326      338  5433.333333  5633.333333     200.0   

              column  
1  RKnee_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         28       39   466.666667   650.000000  183.333333   
5      4        334      338  5566.666667  5633.333333   66.666667   

                  column  
1  LShoulder_speed_event  
5  LShoulder_speed_event  
We need to turn fake events into 0s
We do not need to merge
We do not need to merge
   value  start_idx  end_idx   start_time     end_time  duration  \
3      2        161      173  2683.333333  2883.333333     200.0   

                           column  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
3      2        315      329      5250.0  5483.333333  233.333333   

                           column  
3  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 31-54 in LWrist_speed_event is completely within zero chunk 0-103 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 311-338 in LWrist_speed_event is completely within zero chunk 131-338 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 32-55 in RWrist_speed_event is completely within zero chunk 0-87 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 284-338 in RWrist_speed_event partially overlaps with zero chunk 331-338 in RWrist_vert_vel_movement_event
We do not need to merge
   value  start_idx  end_idx   start_time     end_time   duration  \
3      3        164      166  2733.333333  2766.666667  33.333333   

               column  
3  RWrist_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      2        106      128  1766.666667  2133.333333  366.666667   

               column  
1  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_6_p0_annotated.csv
We need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
   value  start_idx  end_idx  start_time   end_time   duration  \
0      1          0        4         0.0  66.666667  66.666667   

              column  
0  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        130      134  2166.666667  2233.333333   66.666667   
3      2        279      291  4650.000000  4850.000000  200.000000   

               column  
1  LElbow_speed_event  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
0      1          0       16     0.000000   266.666667  266.666667   
2      2         62       64  1033.333333  1066.666667   33.333333   
4      3        126      130  2100.000000  2166.666667   66.666667   
6      4        271      286  4516.666667  4766.666667  250.000000   

                  column  
0  RShoulder_speed_event  
2  RShoulder_speed_event  
4  RShoulder_speed_event  
6  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time    end_time    duration  \
1      1          8       19  133.333333  316.666667  183.333333   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
0      1          0       12         0.0   200.000000  200.000000   
2      2        279      290      4650.0  4833.333333  183.333333   

                  column  
0  LShoulder_speed_event  
2  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
No non-zero rows
   value  start_idx  end_idx   start_time     end_time  duration  \
3      4        163      178  2716.666667  2966.666667     250.0   

                           column  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 3-25 in LWrist_speed_event is completely within zero chunk 0-318 in LWrist_vert_vel_movement_event
Last non-zero chunk 3-25 in LWrist_speed_event is completely within zero chunk 0-318 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 8-24 in RWrist_speed_event is completely within zero chunk 0-52 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 247-288 in RWrist_speed_event partially overlaps with zero chunk 280-318 in RWrist_vert_vel_movement_event
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_70_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        269      282  4483.333333  4700.000000  216.666667   
3      2        301      316  5016.666667  5266.666667  250.000000   

               column  
1  LElbow_speed_event  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 38-53 in LWrist_speed_event is completely within zero chunk 0-455 in LWrist_vert_vel_movement_event
Last non-zero chunk 426-436 in LWrist_speed_event is completely within zero chunk 0-455 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 42-57 in RWrist_speed_event is completely within zero chunk 0-455 in RWrist_vert_vel_movement_event
Last non-zero chunk 426-439 in RWrist_speed_event is completely within zero chunk 0-455 in RWrist_vert_vel_movement_event
   value  start_idx  end_idx   start_time     end_time    duration  \
1      2        262      268  4366.666667  4466.666667  100.000000   
3      3        288      296  4800.000000  4933.333333  133.333333   

               column  
1  RWrist_speed_event  
3  RWrist_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      2        282      292  4700.000000  4866.666667  166.666667   
3      3        326      327  5433.333333  5450.000000   16.666667   

               column  
1  LWrist_speed_event  
3  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_71_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 54-60 in LWrist_speed_event is completely within zero chunk 0-246 in LWrist_vert_vel_movement_event
Last non-zero chunk 218-226 in LWrist_speed_event is completely within zero chunk 0-246 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 52-64 in RWrist_speed_event is completely within zero chunk 0-246 in RWrist_vert_vel_movement_event
Last non-zero chunk 216-235 in RWrist_speed_event is completely within zero chunk 0-246 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_72_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time   duration  \
1      1        255      256      4250.0  4266.666667  16.666667   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        251      259  4183.333333  4316.666667  133.333333   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1         68       73  1133.333333  1216.666667  83.333333   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        247      259  4116.666667  4316.666667     200.0   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 47-66 in LWrist_speed_event is completely within zero chunk 0-301 in LWrist_vert_vel_movement_event
Last non-zero chunk 240-255 in LWrist_speed_event is completely within zero chunk 0-301 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 48-69 in RWrist_speed_event is completely within zero chunk 0-301 in RWrist_vert_vel_movement_event
Last non-zero chunk 240-260 in RWrist_speed_event is completely within zero chunk 0-301 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_73_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time   duration  \
1      1        239      240  3983.333333    4000.0  16.666667   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         39       48       650.0   800.000000  150.000000   
3      2        258      262      4300.0  4366.666667   66.666667   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time   duration  \
1      1         40       45  666.666667     750.0  83.333333   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 40-63 in LWrist_speed_event is completely within zero chunk 0-320 in LWrist_vert_vel_movement_event
Last non-zero chunk 239-251 in LWrist_speed_event is completely within zero chunk 0-320 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 40-66 in RWrist_speed_event is completely within zero chunk 0-320 in RWrist_vert_vel_movement_event
Last non-zero chunk 239-252 in RWrist_speed_event is completely within zero chunk 0-320 in RWrist_vert_vel_movement_event
   value  start_idx  end_idx   start_time  end_time    duration  \
1      2        239      252  3983.333333    4200.0  216.666667   

               column  
1  RWrist_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      2        239      251  3983.333333  4183.333333     200.0   

               column  
1  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_74_p0_annotated.csv
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        296      302  4933.333333  5033.333333     100.0   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1        282      288      4700.0    4800.0     100.0   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1        298      302  4966.666667  5033.333333  66.666667   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 40-55 in LWrist_speed_event is completely within zero chunk 0-302 in LWrist_vert_vel_movement_event
Last non-zero chunk 271-282 in LWrist_speed_event is completely within zero chunk 0-302 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 36-61 in RWrist_speed_event is completely within zero chunk 0-302 in RWrist_vert_vel_movement_event
Last non-zero chunk 276-302 in RWrist_speed_event is completely within zero chunk 0-302 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_75_p0_annotated.csv
We do not need to merge
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1        301      305  5016.666667  5083.333333  66.666667   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1         36       36       600.0     600.0       0.0   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         54       68       900.0  1133.333333  233.333333   
3      2        282      295      4700.0  4916.666667  216.666667   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         52       71  866.666667  1183.333333  316.666667   

                           column  
1  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 35-70 in LWrist_speed_event is completely within zero chunk 0-270 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 271-308 in LWrist_speed_event partially overlaps with zero chunk 299-320 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 34-67 in RWrist_speed_event is completely within zero chunk 0-320 in RWrist_vert_vel_movement_event
Last non-zero chunk 271-305 in RWrist_speed_event is completely within zero chunk 0-320 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_76_p0_annotated.csv
We need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         30       41   500.000000   683.333333  183.333333   
3      2        187      203  3116.666667  3383.333333  266.666667   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         36       48       600.0   800.000000  200.000000   
3      2        186      197      3100.0  3283.333333  183.333333   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        143      154  2383.333333  2566.666667  183.333333   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 33-53 in LWrist_speed_event is completely within zero chunk 0-224 in LWrist_vert_vel_movement_event
Last non-zero chunk 179-196 in LWrist_speed_event is completely within zero chunk 0-224 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 36-56 in RWrist_speed_event is completely within zero chunk 0-224 in RWrist_vert_vel_movement_event
Last non-zero chunk 182-202 in RWrist_speed_event is completely within zero chunk 0-224 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_77_p0_annotated.csv
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        242      253  4033.333333  4216.666667  183.333333   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time    end_time  duration  \
1      1         38       44  633.333333  733.333333     100.0   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1        245      252  4083.333333    4200.0  116.666667   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 24-39 in LWrist_speed_event is completely within zero chunk 0-272 in LWrist_vert_vel_movement_event
Last non-zero chunk 225-243 in LWrist_speed_event is completely within zero chunk 0-272 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 25-46 in RWrist_speed_event is completely within zero chunk 0-272 in RWrist_vert_vel_movement_event
Last non-zero chunk 235-250 in RWrist_speed_event is completely within zero chunk 0-272 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_78_p0_annotated.csv
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1        192      197  3200.000000  3283.333333  83.333333   
3      2        238      240  3966.666667  4000.000000  33.333333   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         42       52   700.000000   866.666667  166.666667   
3      2        205      207  3416.666667  3450.000000   33.333333   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 30-39 in LWrist_speed_event is completely within zero chunk 0-257 in LWrist_vert_vel_movement_event
Last non-zero chunk 223-239 in LWrist_speed_event is completely within zero chunk 0-257 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 26-42 in RWrist_speed_event is completely within zero chunk 0-257 in RWrist_vert_vel_movement_event
Last non-zero chunk 205-231 in RWrist_speed_event is completely within zero chunk 0-257 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_79_p0_annotated.csv
We do not need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        167      170  2783.333333  2833.333333      50.0   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time   duration  \
1      1         27       32       450.0  533.333333  83.333333   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
3      2         79       97  1316.666667  1616.666667  300.000000   
5      4        210      220  3500.000000  3666.666667  166.666667   

               column  
3  RElbow_speed_event  
5  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 34-43 in LWrist_speed_event is completely within zero chunk 0-254 in LWrist_vert_vel_movement_event
Last non-zero chunk 34-43 in LWrist_speed_event is completely within zero chunk 0-254 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 27-55 in RWrist_speed_event partially overlaps with zero chunk 0-28 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 181-225 in RWrist_speed_event is completely within zero chunk 101-254 in RWrist_vert_vel_movement_event
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_7_p0_annotated.csv
We need to merge
We do not need to merge
We need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        374      374  6233.333333  6233.333333       0.0   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
3      3        335      353  5583.333333  5883.333333  300.000000   
5      4        373      392  6216.666667  6533.333333  316.666667   

               column  
3  LElbow_speed_event  
5  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         20       38   333.333333   633.333333  300.000000   
3      2        179      186  2983.333333  3100.000000  116.666667   
5      3        399      402  6650.000000  6700.000000   50.000000   
7      4        441      446  7350.000000  7433.333333   83.333333   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
5  RShoulder_speed_event  
7  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
7      5        441      449      7350.0  7483.333333  133.333333   

               column  
7  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         21       34       350.0   566.666667  216.666667   
3      2        114      131      1900.0  2183.333333  283.333333   
5      3        174      183      2900.0  3050.000000  150.000000   
7      4        438      445      7300.0  7416.666667  116.666667   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
5  LShoulder_speed_event  
7  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
   value  start_idx  end_idx   start_time     end_time  duration  \
3      2        181      190  3016.666667  3166.666667     150.0   

                           column  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 29-36 in LWrist_speed_event is completely within zero chunk 0-102 in LWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 431-446 in LWrist_speed_event is completely within zero chunk 397-472 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 23-39 in RWrist_speed_event is completely within zero chunk 0-105 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 429-448 in RWrist_speed_event is completely within zero chunk 398-472 in RWrist_vert_vel_movement_event
We need to merge
We need to merge
   value  start_idx  end_idx   start_time  end_time    duration  \
3      5        244      255  4066.666667    4250.0  183.333333   

               column  
3  RWrist_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_80_p0_annotated.csv
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time    duration  \
1      1         21       31       350.0  516.666667  166.666667   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time  end_time    duration  \
1      1         37       45  616.666667     750.0  133.333333   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time    duration  \
1      1         22       29  366.666667  483.333333  116.666667   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 24-43 in LWrist_speed_event is completely within zero chunk 0-204 in LWrist_vert_vel_movement_event
Last non-zero chunk 165-170 in LWrist_speed_event is completely within zero chunk 0-204 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 22-47 in RWrist_speed_event is completely within zero chunk 0-204 in RWrist_vert_vel_movement_event
Last non-zero chunk 163-176 in RWrist_speed_event is completely within zero chunk 0-204 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_81_p0_annotated.csv
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         20       36   333.333333   600.000000  266.666667   
3      2        248      248  4133.333333  4133.333333    0.000000   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
3      2         75       86      1250.0  1433.333333  183.333333   

               column  
3  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         21       35   350.000000   583.333333  233.333333   
3      2        178      191  2966.666667  3183.333333  216.666667   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
We need to turn fake events into 0s
No non-zero rows
   value  start_idx  end_idx  start_time    end_time  duration  \
1      1         29       50  483.333333  833.333333     350.0   

                           column  
1  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 24-42 in LWrist_speed_event is completely within zero chunk 0-248 in LWrist_vert_vel_movement_event
Last non-zero chunk 24-42 in LWrist_speed_event is completely within zero chunk 0-248 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 23-55 in RWrist_speed_event is completely within zero chunk 0-70 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 242-248 in RWrist_speed_event is completely within zero chunk 97-248 in RWrist_vert_vel_movement_event
No non-zero rows
   value  start_idx  end_idx   start_time     end_time    duration  \
1      2         76       91  1266.666667  1516.666667  250.000000   
3      3        178      194  2966.666667  3233.333333  266.666667   

               column  
1  RWrist_speed_event  
3  RWrist_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_82_p0_annotated.csv
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         74       86  1233.333333  1433.333333     200.0   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1         72       87      1200.0    1450.0     250.0   
3      2        147      165      2450.0    2750.0     300.0   
5      3        186      195      3100.0    3250.0     150.0   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
5  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
No non-zero rows
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         79       84  1316.666667  1400.000000   83.333333   
5      4        173      185  2883.333333  3083.333333  200.000000   

                           column  
1  RWrist_vert_vel_movement_event  
5  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 35-51 in LWrist_speed_event is completely within zero chunk 0-317 in LWrist_vert_vel_movement_event
Last non-zero chunk 230-246 in LWrist_speed_event is completely within zero chunk 0-317 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 34-51 in RWrist_speed_event is completely within zero chunk 0-110 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 301-317 in RWrist_speed_event is completely within zero chunk 244-317 in RWrist_vert_vel_movement_event
We need to merge
We need to merge
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      2         75       78  1250.000000  1300.000000      50.0   
3      3        115      118  1916.666667  1966.666667      50.0   

               column  
1  LWrist_speed_event  
3  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_83_p0_annotated.csv
We need to merge
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        224      241  3733.333333  4016.666667  283.333333   
3      3        285      290  4750.000000  4833.333333   83.333333   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         36       37       600.0   616.666667   16.666667   
3      2         69       75      1150.0  1250.000000  100.000000   
5      3        294      300      4900.0  5000.000000  100.000000   

               column  
1  LElbow_speed_event  
3  LElbow_speed_event  
5  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        206      209  3433.333333  3483.333333      50.0   

             column  
1  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         19       29   316.666667   483.333333  166.666667   
3      2        223      234  3716.666667  3900.000000  183.333333   
5      3        294      308  4900.000000  5133.333333  233.333333   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
5  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        223      231  3716.666667  3850.000000  133.333333   
3      2        289      302  4816.666667  5033.333333  216.666667   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         16       29   266.666667   483.333333  216.666667   
3      2        296      308  4933.333333  5133.333333  200.000000   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 22-41 in LWrist_speed_event is completely within zero chunk 0-333 in LWrist_vert_vel_movement_event
Last non-zero chunk 267-306 in LWrist_speed_event is completely within zero chunk 0-333 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 22-52 in RWrist_speed_event is completely within zero chunk 0-172 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 136-309 in RWrist_speed_event partially overlaps with zero chunk 272-333 in RWrist_vert_vel_movement_event
We need to merge
We need to merge
   value  start_idx  end_idx   start_time  end_time    duration  \
1      2         62       69  1033.333333    1150.0  116.666667   

               column  
1  RWrist_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time   duration  \
1      2         67       69  1116.666667    1150.0  33.333333   

               column  
1  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_84_p0_annotated.csv
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        137      137  2283.333333  2283.333333       0.0   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        258      274      4300.0  4566.666667  266.666667   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        181      197  3016.666667  3283.333333  266.666667   
3      2        251      258  4183.333333  4300.000000  116.666667   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        200      202  3333.333333  3366.666667   33.333333   
3      2        257      273  4283.333333  4550.000000  266.666667   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 18-35 in LWrist_speed_event is completely within zero chunk 0-302 in LWrist_vert_vel_movement_event
Last non-zero chunk 18-35 in LWrist_speed_event is completely within zero chunk 0-302 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 16-35 in RWrist_speed_event is completely within zero chunk 0-131 in RWrist_vert_vel_movement_event
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 128-272 in RWrist_speed_event partially overlaps with zero chunk 229-302 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_85_p0_annotated.csv
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        231      241      3850.0  4016.666667  166.666667   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1        230      243  3833.333333    4050.0  216.666667   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 233-249 in LWrist_speed_event is completely within zero chunk 0-249 in LWrist_vert_vel_movement_event
Last non-zero chunk 233-249 in LWrist_speed_event is completely within zero chunk 0-249 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 235-249 in RWrist_speed_event is completely within zero chunk 0-249 in RWrist_vert_vel_movement_event
Last non-zero chunk 235-249 in RWrist_speed_event is completely within zero chunk 0-249 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_86_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1        118      119  1966.666667  1983.333333  16.666667   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1        176      186  2933.333333    3100.0  166.666667   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 53-61 in LWrist_speed_event is completely within zero chunk 0-221 in LWrist_vert_vel_movement_event
Last non-zero chunk 165-182 in LWrist_speed_event is completely within zero chunk 0-221 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 51-65 in RWrist_speed_event is completely within zero chunk 0-221 in RWrist_vert_vel_movement_event
Last non-zero chunk 168-184 in RWrist_speed_event is completely within zero chunk 0-221 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      2        165      182      2750.0  3033.333333  283.333333   

               column  
1  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_87_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         39       50       650.0   833.333333  183.333333   
3      2        180      189      3000.0  3150.000000  150.000000   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 32-45 in LWrist_speed_event is completely within zero chunk 0-221 in LWrist_vert_vel_movement_event
Last non-zero chunk 173-178 in LWrist_speed_event is completely within zero chunk 0-221 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 31-46 in RWrist_speed_event is completely within zero chunk 0-221 in RWrist_vert_vel_movement_event
Last non-zero chunk 170-191 in RWrist_speed_event is completely within zero chunk 0-221 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_88_p0_annotated.csv
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        129      143      2150.0  2383.333333  233.333333   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time  end_time    duration  \
1      1         34       42  566.666667     700.0  133.333333   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        128      139  2133.333333  2316.666667  183.333333   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 26-44 in LWrist_speed_event is completely within zero chunk 0-176 in LWrist_vert_vel_movement_event
Last non-zero chunk 121-132 in LWrist_speed_event is completely within zero chunk 0-176 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 28-50 in RWrist_speed_event is completely within zero chunk 0-176 in RWrist_vert_vel_movement_event
Last non-zero chunk 127-135 in RWrist_speed_event is completely within zero chunk 0-176 in RWrist_vert_vel_movement_event
   value  start_idx  end_idx   start_time  end_time    duration  \
1      2        127      135  2116.666667    2250.0  133.333333   

               column  
1  RWrist_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
1      2        121      132  2016.666667    2200.0  183.333333   

               column  
1  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_89_p0_annotated.csv
We need to merge
We do not need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1          2        8    33.333333   133.333333     100.0   
3      2        230      230  3833.333333  3833.333333       0.0   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time    duration  \
1      1         35       46  583.333333  766.666667  183.333333   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
3      2        305      314  5083.333333  5233.333333     150.0   

               column  
3  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        210      219      3500.0  3650.000000  150.000000   
3      2        261      263      4350.0  4383.333333   33.333333   

             column  
1  LHip_speed_event  
3  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time    duration  \
1      1         35       46  583.333333  766.666667  183.333333   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 36-41 in LWrist_speed_event is completely within zero chunk 0-333 in LWrist_vert_vel_movement_event
Last non-zero chunk 282-294 in LWrist_speed_event is completely within zero chunk 0-333 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 34-50 in RWrist_speed_event is completely within zero chunk 0-184 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 298-313 in RWrist_speed_event is completely within zero chunk 253-333 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration  \
1      2         57       57       950.0     950.0       0.0   

               column  
1  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_8_p0_annotated.csv
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        153      166      2550.0  2766.666667  216.666667   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
3      2         91      109  1516.666667  1816.666667     300.0   

               column  
3  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration  \
0      1          0        3         0.0      50.0      50.0   

              column  
0  RKnee_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time    end_time    duration  \
0      1          0       10         0.0  166.666667  166.666667   

                  column  
0  LShoulder_speed_event  
We need to turn fake events into 0s
We do not need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 0-14 in LWrist_speed_event is completely within zero chunk 0-189 in LWrist_vert_vel_movement_event
Last non-zero chunk 152-162 in LWrist_speed_event is completely within zero chunk 0-189 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
no overlap
no overlap
no overlap
no overlap
no overlap
Last non-zero chunk 152-167 in RWrist_speed_event is completely within zero chunk 137-189 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_90_p0_annotated.csv
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration            column
1      1        117      123      1950.0    2050.0     100.0  Head_speed_event
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time    end_time    duration  \
1      1         39       50       650.0  833.333333  183.333333   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time   duration  \
1      1        117      119      1950.0  1983.333333  33.333333   

             column  
1  LHip_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1        125      127  2083.333333  2116.666667  33.333333   

              column  
1  RKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 56-67 in LWrist_speed_event is completely within zero chunk 0-234 in LWrist_vert_vel_movement_event
Last non-zero chunk 56-67 in LWrist_speed_event is completely within zero chunk 0-234 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 37-156 in RWrist_speed_event partially overlaps with zero chunk 0-39 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 164-187 in RWrist_speed_event is completely within zero chunk 141-234 in RWrist_vert_vel_movement_event
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_91_p0_annotated.csv
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time    duration  \
1      1          9       25       150.0  416.666667  266.666667   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time   duration  \
1      1        117      118      1950.0  1966.666667  16.666667   

             column  
1  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
3      3        173      179  2883.333333  2983.333333     100.0   

                  column  
3  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         31       51   516.666667   850.000000  333.333333   
3      3        101      120  1683.333333  2000.000000  316.666667   
5      4        170      178  2833.333333  2966.666667  133.333333   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
5  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 27-42 in LWrist_speed_event is completely within zero chunk 0-188 in LWrist_vert_vel_movement_event
Last non-zero chunk 152-164 in LWrist_speed_event is completely within zero chunk 0-188 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 31-43 in RWrist_speed_event is completely within zero chunk 0-48 in RWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 160-171 in RWrist_speed_event is completely within zero chunk 139-188 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_92_p1_annotated.csv
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1        248      255  4133.333333    4250.0  116.666667   

              column  
1  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        157      165  2616.666667  2750.000000  133.333333   
3      3        204      214  3400.000000  3566.666667  166.666667   
5      5        248      248  4133.333333  4133.333333    0.000000   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
5  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1        235      255  3916.666667    4250.0  333.333333   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1        234      255      3900.0    4250.0     350.0   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
No non-zero rows
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1        238      255  3966.666667    4250.0  283.333333   

                           column  
1  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 233-255 in LWrist_speed_event is completely within zero chunk 0-255 in LWrist_vert_vel_movement_event
Last non-zero chunk 233-255 in LWrist_speed_event is completely within zero chunk 0-255 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 228-255 in RWrist_speed_event is completely within zero chunk 0-255 in RWrist_vert_vel_movement_event
Last non-zero chunk 228-255 in RWrist_speed_event is completely within zero chunk 0-255 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_93_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        105      105  1750.000000  1750.000000    0.000000   
3      2        161      178  2683.333333  2966.666667  283.333333   
5      4        246      250  4100.000000  4166.666667   66.666667   
7      5        293      294  4883.333333  4900.000000   16.666667   

              column  
1  RHeel_speed_event  
3  RHeel_speed_event  
5  RHeel_speed_event  
7  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
3      2        202      217  3366.666667  3616.666667  250.000000   
5      3        288      293  4800.000000  4883.333333   83.333333   

               column  
3  LElbow_speed_event  
5  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
1      1         78       83  1300.000000  1383.333333  83.333333   
5      5        260      265  4333.333333  4416.666667  83.333333   

             column  
1  Head_speed_event  
5  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         41       56   683.333333   933.333333     250.0   
5      4        288      306  4800.000000  5100.000000     300.0   

                  column  
1  RShoulder_speed_event  
5  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         51       59   850.000000   983.333333  133.333333   
5      5        286      298  4766.666667  4966.666667  200.000000   

               column  
1  RElbow_speed_event  
5  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time   duration  \
3      3        293      297  4883.333333    4950.0  66.666667   

             column  
3  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time    duration  \
1      1         40       56  666.666667  933.333333  266.666667   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
We do not need to merge
We do not need to merge
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         44       56   733.333333   933.333333  200.000000   
3      2        285      302  4750.000000  5033.333333  283.333333   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        295      298  4916.666667  4966.666667      50.0   

                           column  
1  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 36-61 in LWrist_speed_event is completely within zero chunk 0-312 in LWrist_vert_vel_movement_event
Last non-zero chunk 276-304 in LWrist_speed_event is completely within zero chunk 0-312 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 35-64 in RWrist_speed_event is completely within zero chunk 0-312 in RWrist_vert_vel_movement_event
Last non-zero chunk 279-305 in RWrist_speed_event is completely within zero chunk 0-312 in RWrist_vert_vel_movement_event
   value  start_idx  end_idx   start_time  end_time   duration  \
1      2        155      159  2583.333333    2650.0  66.666667   
3      3        209      210  3483.333333    3500.0  16.666667   

               column  
1  RWrist_speed_event  
3  RWrist_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      2        167      176  2783.333333  2933.333333  150.000000   
3      3        199      215  3316.666667  3583.333333  266.666667   

               column  
1  LWrist_speed_event  
3  LWrist_speed_event  
We need to turn fake events into 0s
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_94_p1_annotated.csv
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1         55       69   916.666667    1150.0  233.333333   
3      2        195      198  3250.000000    3300.0   50.000000   

               column  
1  LElbow_speed_event  
3  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        238      244  3966.666667  4066.666667     100.0   

             column  
1  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         53       55   883.333333   916.666667   33.333333   
3      2        140      160  2333.333333  2666.666667  333.333333   
7      4        266      267  4433.333333  4450.000000   16.666667   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
7  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         58       77  966.666667  1283.333333  316.666667   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time    end_time   duration  \
1      1         51       53       850.0  883.333333  33.333333   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
We need to merge
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         47       60   783.333333  1000.000000  216.666667   
3      2        183      197  3050.000000  3283.333333  233.333333   
5      3        260      267  4333.333333  4450.000000  116.666667   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
5  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 40-66 in LWrist_speed_event is completely within zero chunk 0-184 in LWrist_vert_vel_movement_event
no overlap
no overlap
Last non-zero chunk 254-267 in LWrist_speed_event is completely within zero chunk 221-267 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 43-72 in RWrist_speed_event is completely within zero chunk 0-267 in RWrist_vert_vel_movement_event
Last non-zero chunk 256-267 in RWrist_speed_event is completely within zero chunk 0-267 in RWrist_vert_vel_movement_event
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_95_p1_annotated.csv
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        129      143  2150.000000  2383.333333  233.333333   
5      6        295      296  4916.666667  4933.333333   16.666667   

              column  
1  RHeel_speed_event  
5  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time   duration  \
1      1        144      148      2400.0  2466.666667  66.666667   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         65       68  1083.333333  1133.333333      50.0   

             column  
1  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
5      3        189      203  3150.000000  3383.333333  233.333333   
9      6        355      359  5916.666667  5983.333333   66.666667   

                  column  
5  RShoulder_speed_event  
9  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         65       71  1083.333333  1183.333333  100.000000   
3      2        206      209  3433.333333  3483.333333   50.000000   
5      3        300      310  5000.000000  5166.666667  166.666667   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
5  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1         62       69  1033.333333    1150.0  116.666667   

             column  
1  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         58       76   966.666667  1266.666667  300.000000   
5      3        194      214  3233.333333  3566.666667  333.333333   
7      4        301      321  5016.666667  5350.000000  333.333333   

                  column  
1  LShoulder_speed_event  
5  LShoulder_speed_event  
7  LShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         58       70   966.666667  1166.666667  200.000000   
3      2        298      314  4966.666667  5233.333333  266.666667   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        307      313  5116.666667  5216.666667     100.0   

                           column  
1  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 50-74 in LWrist_speed_event is completely within zero chunk 0-359 in LWrist_vert_vel_movement_event
Last non-zero chunk 291-317 in LWrist_speed_event is completely within zero chunk 0-359 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 55-80 in RWrist_speed_event is completely within zero chunk 0-359 in RWrist_vert_vel_movement_event
Last non-zero chunk 291-318 in RWrist_speed_event is completely within zero chunk 0-359 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_96_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We do not need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         78       94      1300.0  1566.666667  266.666667   

              column  
1  RHeel_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         54       64   900.000000  1066.666667  166.666667   
3      2        184      195  3066.666667  3250.000000  183.333333   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         57       69   950.000000  1150.000000  200.000000   
3      2        284      300  4733.333333  5000.000000  266.666667   
5      3        330      346  5500.000000  5766.666667  266.666667   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
5  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        297      320      4950.0  5333.333333  383.333333   

             column  
1  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1         54       63   900.000000    1050.0  150.000000   
3      2        206      219  3433.333333    3650.0  216.666667   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
We need to turn fake events into 0s
No non-zero rows
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         52       62   866.666667  1033.333333  166.666667   
3      2        317      334  5283.333333  5566.666667  283.333333   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 44-67 in LWrist_speed_event is completely within zero chunk 0-360 in LWrist_vert_vel_movement_event
Last non-zero chunk 295-335 in LWrist_speed_event is completely within zero chunk 0-360 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 45-72 in RWrist_speed_event is completely within zero chunk 0-360 in RWrist_vert_vel_movement_event
Last non-zero chunk 312-343 in RWrist_speed_event is completely within zero chunk 0-360 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_97_p1_annotated.csv
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        382      391  6366.666667  6516.666667     150.0   

              column  
1  LKnee_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         11       11   183.333333   183.333333       0.0   
5      7        373      376  6216.666667  6266.666667      50.0   

              column  
1  RHeel_speed_event  
5  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1        381      391      6350.0  6516.666667  166.666667   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        373      391  6216.666667  6516.666667     300.0   

                  column  
1  RShoulder_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time  end_time   duration  \
1      1        211      213  3516.666667    3550.0  33.333333   

             column  
1  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1        192      195  3200.000000  3250.000000   50.000000   
3      2        374      391  6233.333333  6516.666667  283.333333   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
We need to turn fake events into 0s
We do not need to merge
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1        366      381      6100.0    6350.0     250.0   

                           column  
1  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        365      368  6083.333333  6133.333333      50.0   

                           column  
1  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 362-391 in LWrist_speed_event is completely within zero chunk 0-391 in LWrist_vert_vel_movement_event
Last non-zero chunk 362-391 in LWrist_speed_event is completely within zero chunk 0-391 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 366-391 in RWrist_speed_event is completely within zero chunk 0-391 in RWrist_vert_vel_movement_event
Last non-zero chunk 366-391 in RWrist_speed_event is completely within zero chunk 0-391 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_98_p1_annotated.csv
We need to merge
We need to merge
We need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time  duration  \
1      1        297      303      4950.0    5050.0     100.0   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
7      5        322      323  5366.666667  5383.333333  16.666667   

             column  
7  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         56       66   933.333333  1100.000000  166.666667   
3      2        107      119  1783.333333  1983.333333  200.000000   
5      3        201      214  3350.000000  3566.666667  216.666667   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
5  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        293      305  4883.333333  5083.333333     200.0   

               column  
1  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time  end_time    duration  \
1      1         56       66  933.333333    1100.0  166.666667   

                  column  
1  LShoulder_speed_event  
We need to turn fake events into 0s
We do not need to merge
No non-zero rows
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         52       61   866.666667  1016.666667  150.000000   
3      2        293      309  4883.333333  5150.000000  266.666667   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 46-66 in LWrist_speed_event is completely within zero chunk 0-323 in LWrist_vert_vel_movement_event
Last non-zero chunk 291-312 in LWrist_speed_event is completely within zero chunk 0-323 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 48-71 in RWrist_speed_event is completely within zero chunk 0-323 in RWrist_vert_vel_movement_event
Last non-zero chunk 287-311 in RWrist_speed_event is completely within zero chunk 0-323 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_99_p1_annotated.csv
We need to merge
We need to merge
We do not need to merge
We need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         99      116  1650.000000  1933.333333  283.333333   
5      6        316      321  5266.666667  5350.000000   83.333333   

              column  
1  RHeel_speed_event  
5  RHeel_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        134      143  2233.333333  2383.333333     150.0   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time   duration  \
5      5        367      371  6116.666667  6183.333333  66.666667   

             column  
5  Head_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         33       47   550.000000   783.333333  233.333333   
5      3        220      239  3666.666667  3983.333333  316.666667   
7      4        338      354  5633.333333  5900.000000  266.666667   

                  column  
1  RShoulder_speed_event  
5  RShoulder_speed_event  
7  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         46       56   766.666667   933.333333  166.666667   
3      2        116      131  1933.333333  2183.333333  250.000000   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx  start_time    end_time   duration  \
1      1         39       41       650.0  683.333333  33.333333   

             column  
1  LHip_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1         32       47   533.333333   783.333333     250.0   
5      5        337      355  5616.666667  5916.666667     300.0   

                  column  
1  LShoulder_speed_event  
5  LShoulder_speed_event  
We need to turn fake events into 0s
We do not need to merge
We do not need to merge
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         32       44   533.333333   733.333333  200.000000   
3      2        344      357  5733.333333  5950.000000  216.666667   

                           column  
1  RWrist_vert_vel_movement_event  
3  RWrist_vert_vel_movement_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time   duration  \
1      1         25       30   416.666667     500.0  83.333333   
3      2        355      357  5916.666667    5950.0  33.333333   

                           column  
1  LWrist_vert_vel_movement_event  
3  LWrist_vert_vel_movement_event  
We need to turn fake events into 0s
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 22-49 in LWrist_speed_event is completely within zero chunk 0-371 in LWrist_vert_vel_movement_event
Last non-zero chunk 340-360 in LWrist_speed_event is completely within zero chunk 0-371 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 29-52 in RWrist_speed_event is completely within zero chunk 0-371 in RWrist_vert_vel_movement_event
Last non-zero chunk 338-359 in RWrist_speed_event is completely within zero chunk 0-371 in RWrist_vert_vel_movement_event
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\mt_centered_0_2_9_p0_annotated.csv
We do not need to merge
We do not need to merge
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      1        188      188  3133.333333  3133.333333       0.0   

               column  
1  LElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx  start_time     end_time    duration  \
1      1         15       18       250.0   300.000000   50.000000   
3      2        180      194      3000.0  3233.333333  233.333333   

                  column  
1  RShoulder_speed_event  
3  RShoulder_speed_event  
We need to turn fake events into 0s
   value  start_idx  end_idx   start_time  end_time    duration  \
1      1         17       21   283.333333     350.0   66.666667   
3      2        184      192  3066.666667    3200.0  133.333333   

               column  
1  RElbow_speed_event  
3  RElbow_speed_event  
We need to turn fake events into 0s
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time    duration  \
1      1         14       23   233.333333   383.333333  150.000000   
3      2        187      194  3116.666667  3233.333333  116.666667   

                  column  
1  LShoulder_speed_event  
3  LShoulder_speed_event  
We need to turn fake events into 0s
No non-zero rows
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
Checking LWrist_event vs LWrist_vv_event
First non-zero chunk 11-27 in LWrist_speed_event is completely within zero chunk 0-232 in LWrist_vert_vel_movement_event
Last non-zero chunk 172-190 in LWrist_speed_event is completely within zero chunk 0-232 in LWrist_vert_vel_movement_event

Checking RWrist_event vs RWrist_vv_event
First non-zero chunk 15-27 in RWrist_speed_event is completely within zero chunk 0-232 in RWrist_vert_vel_movement_event
Last non-zero chunk 177-195 in RWrist_speed_event is completely within zero chunk 0-232 in RWrist_vert_vel_movement_event
No non-zero rows
Empty DataFrame
Columns: [value, start_idx, end_idx, start_time, end_time, duration, column]
Index: []
No fake events found
   value  start_idx  end_idx   start_time     end_time  duration  \
1      2        172      190  2866.666667  3166.666667     300.0   

               column  
1  LWrist_speed_event  
We need to turn fake events into 0s

Merge them into tiers¶

In [331]:
elanfiles = glob.glob(annofolder + '/*ELAN_anno.csv')

# group mapping
group_mapping = {
    'Head_speed_event': 'head_mov',
    'RShoulder_speed_event': 'upper_body',
    'LShoulder_speed_event': 'upper_body',
    'RWrist_speed_event': 'arms',
    'LWrist_speed_event': 'arms',
    'RElbow_speed_event': 'arms',
    'LElbow_speed_event': 'arms',
    'RHip_speed_event': 'lower_body',
    'LHip_speed_event': 'lower_body',
    'RKnee_speed_event': 'lower_body',
    'LKnee_speed_event': 'lower_body',
    'RAnkle_speed_event': 'lower_body',
    'LAnkle_speed_event': 'lower_body',
    'RHeel_speed_event': 'lower_body',
    'LHeel_speed_event': 'lower_body'}
    # skipping elbows for now
    
# Extract unique groups
groups = set(group_mapping.values())

# for each file, annotate the groups
for file in elanfiles:
    print('working on ' + file)
    # load the file
    df = pd.read_csv(file)

    # Initialize new columns for each group with 'nomovement'
    for group in groups:
        df[group] = 'nomovement'

    # Iterate over each row and update the group columns
    for index, row in df.iterrows():
        for keypoint, status in row.items():
            if keypoint in group_mapping and status == 'movement':
                group = group_mapping[keypoint]
                df.at[index, group] = 'movement'

    # create column 'movement_in_trial' that checks first row of arms, lower_body, upper_body, head_mov where there is a movement in any of those and last one, and everything in between is movement, elsewhere it is nomovement
    df['movement_in_trial'] = 'nomovement'
    # check what is the first row in arms, lower_body, upper_body, head_mov where there is movement
    # first check whether there is any value 'movement' in any of the columns or it's only 'nomovement', if yes, get the index of the first one
    if 'movement' in df['arms'].tolist() or 'movement' in df['lower_body'].tolist() or 'movement' in df['upper_body'].tolist():
        first_movement = df[(df['arms'] == 'movement') | (df['lower_body'] == 'movement') | (df['upper_body'] == 'movement')].index[0] #| (df['head_mov'] == 'movement')].index[0]
        last_movement = df[(df['arms'] == 'movement') | (df['lower_body'] == 'movement') | (df['upper_body'] == 'movement')].index[-1] #| (df['head_mov'] == 'movement')].index[-1]

        # everything in between is movement
        df.loc[first_movement:last_movement, 'movement_in_trial'] = 'movement'
    else:
        print('No movement in this trial')

    # in arms check first row of movement and last and everything in between is a movement ## FLAGG maybe this will neeed to be later optimized if there is a big gap between movements
    if 'movement' in df['arms'].tolist():
        first_arm = df[df['arms'] == 'movement'].index[0]
        last_arm = df[df['arms'] == 'movement'].index[-1]

        df.loc[first_arm:last_arm, 'arms'] = 'movement'

    #head
    if 'movement' in df['head_mov'].tolist():
        first_head = df[df['head_mov'] == 'movement'].index[0]
        last_head = df[df['head_mov'] == 'movement'].index[-1]

        df.loc[first_head:last_head, 'head_mov'] = 'movement'

    # upper
    if 'movement' in df['upper_body'].tolist():
        first_upper = df[df['upper_body'] == 'movement'].index[0]
        last_upper = df[df['upper_body'] == 'movement'].index[-1]

        df.loc[first_upper:last_upper, 'upper_body'] = 'movement'

    # lower
    if 'movement' in df['lower_body'].tolist():
        first_lower = df[df['lower_body'] == 'movement'].index[0]
        last_lower = df[df['lower_body'] == 'movement'].index[-1]

        df.loc[first_lower:last_lower, 'lower_body'] = 'movement'

    # get rid of all event columns
    df = df[[x for x in df.columns if 'event' not in x]]

    # save the annotated file
    df.to_csv(file.replace('ELAN_anno', 'ELAN_tiers'), index=False)
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_0_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_10_p1_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_11_p1_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_12_p1_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_13_p1_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_14_p1_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_15_p1_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_16_p1_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_17_p1_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_18_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_19_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_1_p0_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_20_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_21_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_22_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_23_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_24_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_25_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_26_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_27_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_28_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_29_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_2_p0_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_30_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_31_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_32_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_33_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_35_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_36_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_37_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_38_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_39_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_3_p0_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_40_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_41_p0_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_42_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_43_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_44_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_45_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_46_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_47_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_48_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_49_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_4_p0_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_50_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_51_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_52_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_53_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_5_p0_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_6_p0_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_7_p0_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_8_p0_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_9_p1_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_tpose_0_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_tpose_1_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_0_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_100_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_101_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_102_p1_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_103_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_104_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_105_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_106_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_107_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_108_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_109_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_10_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_110_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_111_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_112_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_113_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_11_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_12_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_13_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_14_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_15_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_16_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_17_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_18_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_19_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_1_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_20_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_21_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_22_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_23_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_24_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_25_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_26_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_27_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_28_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_29_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_2_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_30_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_31_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_32_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_33_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_34_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_35_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_36_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_37_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_38_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_39_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_3_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_40_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_41_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_43_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_44_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_45_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_46_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_47_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_48_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_49_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_4_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_50_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_51_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_52_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_53_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_54_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_55_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_56_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_57_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_58_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_59_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_5_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_60_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_61_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_62_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_63_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_64_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_65_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_67_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_68_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_69_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_6_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_70_p0_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_71_p0_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_72_p0_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_73_p0_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_74_p0_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_75_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_76_p0_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_77_p0_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_78_p0_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_79_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_7_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_80_p0_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_81_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_82_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_83_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_84_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_85_p0_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_86_p0_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_87_p0_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_88_p0_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_89_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_8_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_90_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_91_p0_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_92_p1_ELAN_anno.csv
No movement in this trial
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_93_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_94_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_95_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_96_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_97_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_98_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_99_p1_ELAN_anno.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_9_p0_ELAN_anno.csv
No movement in this trial

Create ELAN files¶

function¶

In [27]:
from xml.etree import ElementTree as ET

# Function to create ELAN file
def create_eaf(chunks_dict, output_file):
    annotation_document = ET.Element('ANNOTATION_DOCUMENT', {
        'xmlns:xsi': "http://www.w3.org/2001/XMLSchema-instance",
        'AUTHOR': "",
        'DATE': "2024-05-28T11:54:22+01:00",
        'FORMAT': "3.0",
        'VERSION': "3.0",
        'xsi:noNamespaceSchemaLocation': "http://www.mpi.nl/tools/elan/EAFv3.0.xsd"
    })
    
    header = ET.SubElement(annotation_document, 'HEADER', {'MEDIA_FILE': "", 'TIME_UNITS': "milliseconds"})
    ET.SubElement(header, 'MEDIA_DESCRIPTOR', {
        'MEDIA_URL': "file:///path_to_your_file.wav",
        'MIME_TYPE': "audio/x-wav"
    })
    ET.SubElement(header, 'MEDIA_DESCRIPTOR', {
        'MEDIA_URL': "file:///path_to_your_file.avi",
        'MIME_TYPE': "video/*",
        'RELATIVE_MEDIA_URL': "./path_to_your_file.avi"
    })
    ET.SubElement(header, 'PROPERTY', {'NAME': "URN"}).text = "urn:nl-mpi-tools-elan-eaf:73467978-4930-486d-a56b-fa6acb05e357"
    ET.SubElement(header, 'PROPERTY', {'NAME': "lastUsedAnnotationId"}).text = "3"
    
    time_order = ET.SubElement(annotation_document, 'TIME_ORDER')
    ts_id_counter = 1
    time_slot_map = {}
    
    # Generate time slots for all chunks
    for group, group_chunks in chunks_dict.items():
        for _, chunk in group_chunks.iterrows():
            start_ts_id = f'ts{ts_id_counter}'
            if chunk['start_time'] not in time_slot_map:
                ET.SubElement(time_order, 'TIME_SLOT', {'TIME_SLOT_ID': start_ts_id, 'TIME_VALUE': str(int(chunk['start_time']))})
                time_slot_map[chunk['start_time']] = start_ts_id
                ts_id_counter += 1
            else:
                start_ts_id = time_slot_map[chunk['start_time']]

            end_ts_id = f'ts{ts_id_counter}'
            if chunk['end_time'] not in time_slot_map:
                ET.SubElement(time_order, 'TIME_SLOT', {'TIME_SLOT_ID': end_ts_id, 'TIME_VALUE': str(int(chunk['end_time']))})
                time_slot_map[chunk['end_time']] = end_ts_id
                ts_id_counter += 1
            else:
                end_ts_id = time_slot_map[chunk['end_time']]

    # Create the tiers
    for group, group_chunks in chunks_dict.items():
        tier = ET.SubElement(annotation_document, 'TIER', {
            'LINGUISTIC_TYPE_REF': "mov_detect",
            'TIER_ID': group
        })
        for i, chunk in group_chunks.iterrows():
            start_ts_id = time_slot_map[chunk['start_time']]
            end_ts_id = time_slot_map[chunk['end_time']]
            annotation_id = f'{group}_a{i + 1}'
            annotation = ET.SubElement(tier, 'ANNOTATION')
            alignable_annotation = ET.SubElement(annotation, 'ALIGNABLE_ANNOTATION', {
                'ANNOTATION_ID': annotation_id,
                'TIME_SLOT_REF1': start_ts_id,
                'TIME_SLOT_REF2': end_ts_id
            })
            ET.SubElement(alignable_annotation, 'ANNOTATION_VALUE').text = chunk['value']
    
    # Add constraints and linguistic types
    ET.SubElement(annotation_document, 'LINGUISTIC_TYPE', {
        'GRAPHIC_REFERENCES': "false",
        'LINGUISTIC_TYPE_ID': "mov_detect",
        'TIME_ALIGNABLE': "true"
    })
    constraints = [
        ("Time subdivision of parent annotation's time interval, no time gaps allowed within this interval", "Time_Subdivision"),
        ("Symbolic subdivision of a parent annotation. Annotations referring to the same parent are ordered", "Symbolic_Subdivision"),
        ("1-1 association with a parent annotation", "Symbolic_Association"),
        ("Time alignable annotations within the parent annotation's time interval, gaps are allowed", "Included_In")
    ]
    for desc, stereotype in constraints:
        ET.SubElement(annotation_document, 'CONSTRAINT', {'DESCRIPTION': desc, 'STEREOTYPE': stereotype})
    
    tree = ET.ElementTree(annotation_document)
    tree.write(output_file, encoding='UTF-8', xml_declaration=True)

apply function¶

In [332]:
elanfiles = glob.glob(annofolder + '/*ELAN_tiers.csv')

for file in elanfiles:
    print('working on ' + file)
    # load the file
    df = pd.read_csv(file)

    # get chunks
    parent_chunks = get_chunks(df, 'Time', 'movement_in_trial')
    head_chunks = get_chunks(df, 'Time', 'head_mov')
    upper_body_chunks = get_chunks(df, 'Time', 'upper_body')
    arms_chunks = get_chunks(df, 'Time', 'arms')
    lower_body_chunks = get_chunks(df, 'Time', 'lower_body')

    # Adjust end time of each chunk to be the start time of the next chunk
    parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
    head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
    upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
    arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
    lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]

    # chunk dictionary
    chunks_dict = {
        'movement_in_trial': parent_chunks,
        'head_mov': head_chunks,
        'upper_body': upper_body_chunks,
        'arms': arms_chunks,
        'lower_body': lower_body_chunks
    }
    
    # create the eaf file
    create_eaf(chunks_dict, file.replace('.csv', '.eaf'))
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_0_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_10_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_11_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_12_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_13_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_14_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_15_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_16_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_17_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_18_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_19_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_1_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_20_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_21_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_22_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_23_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_24_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_25_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_26_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_27_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_28_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_29_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_2_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_30_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_31_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_32_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_33_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_35_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_36_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_37_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_38_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_39_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_3_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_40_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_41_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_42_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_43_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_44_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_45_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_46_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_47_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_48_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_49_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_4_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_50_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_51_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_52_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_53_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_5_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_6_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_7_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_8_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_9_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_tpose_0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_tpose_1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_0_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_100_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_101_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_102_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_103_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_104_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_105_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_106_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_107_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_108_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_109_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_10_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_110_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_111_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_112_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_113_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_11_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_12_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_13_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_14_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_15_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_16_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_17_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_18_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_19_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_1_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_20_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_21_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_22_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_23_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_24_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_25_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_26_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_27_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_28_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_29_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_2_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_30_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_31_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_32_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_33_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_34_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_35_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_36_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_37_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_38_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_39_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_3_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_40_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_41_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_43_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_44_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_45_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_46_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_47_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_48_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_49_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_4_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_50_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_51_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_52_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_53_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_54_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_55_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_56_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_57_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_58_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_59_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_5_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_60_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_61_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_62_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_63_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_64_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_65_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_67_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_68_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_69_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_6_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_70_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_71_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_72_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_73_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_74_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_75_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_76_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_77_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_78_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_79_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_7_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_80_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_81_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_82_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_83_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_84_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_85_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_86_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_87_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_88_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_89_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_8_p0_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_90_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_91_p0_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_92_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_93_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_94_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_95_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_96_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_97_p1_ELAN_tiers.csv
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  parent_chunks['end_time'][0:-1] = parent_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  head_chunks['end_time'][0:-1] = head_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  upper_body_chunks['end_time'][0:-1] = upper_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  arms_chunks['end_time'][0:-1] = arms_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
C:\Users\kadava\AppData\Local\Temp\ipykernel_14416\3788601205.py:20: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lower_body_chunks['end_time'][0:-1] = lower_body_chunks['start_time'][1:]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_98_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_99_p1_ELAN_tiers.csv
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_9_p0_ELAN_tiers.csv

Editing eaf files for manual annotation¶

function¶

In [3]:
import xml.etree.ElementTree as ET
import glob

def add_nomovement_annotations(xml_file_path, newfilepath):
    # Load the XML file
    tree = ET.parse(xml_file_path)
    root = tree.getroot()

    # Extract all time slots
    time_slots = {}
    for time_slot in root.find('TIME_ORDER').findall('TIME_SLOT'):
        time_slots[time_slot.attrib['TIME_SLOT_ID']] = int(time_slot.attrib['TIME_VALUE'])

    # Sort time slots by TIME_VALUE
    sorted_time_slots = sorted(time_slots.items(), key=lambda x: x[1])
    time_slot_ids = [ts[0] for ts in sorted_time_slots]
    time_values = [ts[1] for ts in sorted_time_slots]

    # Loop over all tiers
    for tier in root.findall('TIER'):
        annotations = tier.findall('ANNOTATION/ALIGNABLE_ANNOTATION')

        if not annotations:
            # If no annotations exist, add a single 'nomovement' annotation covering the whole tier
            new_annotation = ET.Element('ANNOTATION')
            alignable_annotation = ET.SubElement(new_annotation, 'ALIGNABLE_ANNOTATION')
            alignable_annotation.set('TIME_SLOT_REF1', time_slot_ids[0])
            alignable_annotation.set('TIME_SLOT_REF2', time_slot_ids[-1])
            annotation_value = ET.SubElement(alignable_annotation, 'ANNOTATION_VALUE')
            annotation_value.text = 'nomovement'
            tier.append(new_annotation)
        else:
            # Sort annotations by start time
            sorted_annotations = sorted(annotations, key=lambda x: time_slots[x.attrib['TIME_SLOT_REF1']])
            
            # Handle the first annotation
            first_annotation = sorted_annotations[0]
            first_start_time = time_slots[first_annotation.attrib['TIME_SLOT_REF1']]
            if first_start_time > time_values[0]:
                new_annotation = ET.Element('ANNOTATION')
                alignable_annotation = ET.SubElement(new_annotation, 'ALIGNABLE_ANNOTATION')
                alignable_annotation.set('TIME_SLOT_REF1', time_slot_ids[0])
                alignable_annotation.set('TIME_SLOT_REF2', first_annotation.attrib['TIME_SLOT_REF1'])
                annotation_value = ET.SubElement(alignable_annotation, 'ANNOTATION_VALUE')
                annotation_value.text = 'nomovement'
                tier.append(new_annotation)

            # Handle gaps between annotations
            for i in range(len(sorted_annotations) - 1):
                current_annotation = sorted_annotations[i]
                next_annotation = sorted_annotations[i + 1]
                current_end_time = time_slots[current_annotation.attrib['TIME_SLOT_REF2']]
                next_start_time = time_slots[next_annotation.attrib['TIME_SLOT_REF1']]
                if current_end_time < next_start_time:
                    new_annotation = ET.Element('ANNOTATION')
                    alignable_annotation = ET.SubElement(new_annotation, 'ALIGNABLE_ANNOTATION')
                    alignable_annotation.set('TIME_SLOT_REF1', current_annotation.attrib['TIME_SLOT_REF2'])
                    alignable_annotation.set('TIME_SLOT_REF2', next_annotation.attrib['TIME_SLOT_REF1'])
                    annotation_value = ET.SubElement(alignable_annotation, 'ANNOTATION_VALUE')
                    annotation_value.text = 'nomovement'
                    tier.append(new_annotation)

            # Handle the last annotation
            last_annotation = sorted_annotations[-1]
            last_end_time = time_slots[last_annotation.attrib['TIME_SLOT_REF2']]
            if last_end_time < time_values[-1]:
                new_annotation = ET.Element('ANNOTATION')
                alignable_annotation = ET.SubElement(new_annotation, 'ALIGNABLE_ANNOTATION')
                alignable_annotation.set('TIME_SLOT_REF1', last_annotation.attrib['TIME_SLOT_REF2'])
                alignable_annotation.set('TIME_SLOT_REF2', time_slot_ids[-1])
                annotation_value = ET.SubElement(alignable_annotation, 'ANNOTATION_VALUE')
                annotation_value.text = 'nomovement'
                tier.append(new_annotation)

    # Save the modified XML file as a new file
    tree.write(newfilepath, encoding='UTF-8', xml_declaration=True)

apply function¶

In [4]:
manualanno_folder_r1 = curfolder + '/ManualAnno/R1/'            # ola
manualanno_folder_r3 = curfolder + '/ManualAnno/R3/'            # gillian

manualannofiles1 = glob.glob(manualanno_folder_r1 + '/*.eaf')
manualannofiles3 = glob.glob(manualanno_folder_r3 + '/*.eaf')


for file in manualannofiles1:
    print('working on ' + file)

    # new filename is without third part of the name
    newfile = file.split('\\')[-1]
    chunks = newfile.split('_')
    if 'corrected' in file:
        if 'c0' in file or 'c1' in file or 'c2' in file:
            newfile = '_'.join(chunks[:-4])
        else:
            newfile = '_'.join(chunks[:-3])
    else:
        if 'c0' in file or 'c1' in file or 'c2' in file:
            newfile = '_'.join(chunks[:-3])
        else:
            newfile = '_'.join(chunks[:-2]) 

    # replace trial_ with _
    newfile = newfile.replace('trial_', '')
    # add filepath
    newfile = manualanno_folder_r1 + newfile + '_ELAN_tiers.eaf'

    add_nomovement_annotations(file, newfile)
    
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_11_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_12_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_13_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_14_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_15_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_16_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_17_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_20_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_21_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_22_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_23_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_24_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_25_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_26_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_29_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_30_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_32_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_33_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_34_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_38_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_48_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_49_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_17_p1_luidruchtig_geluiden.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_20_p0_verdrietig_combinatie.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_1_trial_21_p0_koud_combinatie.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_61_p1_lachen_gebaren_c0.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_62_p1_zoet_gebaren_c0.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_63_p1_zoet_gebaren_c1.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_64_p1_zoet_gebaren_c2.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_65_p1_slapen_gebaren_c0.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_66_p1_berg_gebaren_c0.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_73_p0_geur_geluiden_c0.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R1\0_2_trial_74_p0_geur_geluiden_c1.eaf

Text files for interrater agreement¶

In [10]:
import xml.etree.ElementTree as ET

def parse_eaf_file(eaf_file, rel_tiers):
    tree = ET.parse(eaf_file)
    root = tree.getroot()

    time_order = root.find('TIME_ORDER')
    time_slots = {time_slot.attrib['TIME_SLOT_ID']: time_slot.attrib['TIME_VALUE'] for time_slot in time_order}

    annotations = []
    relevant_tiers = {rel_tiers}
    for tier in root.findall('TIER'):
        tier_id = tier.attrib['TIER_ID']
        if tier_id in relevant_tiers:
            for annotation in tier.findall('ANNOTATION/ALIGNABLE_ANNOTATION'):
                print(annotation)
                # Ensure required attributes are present
                if 'TIME_SLOT_REF1' in annotation.attrib and 'TIME_SLOT_REF2' in annotation.attrib:
                    ts_ref1 = annotation.attrib['TIME_SLOT_REF1']
                    ts_ref2 = annotation.attrib['TIME_SLOT_REF2']
                    # Get annotation ID if it exists, otherwise set to None
                    ann_id = annotation.attrib.get('ANNOTATION_ID', None)
                    annotation_value = annotation.find('ANNOTATION_VALUE').text.strip()
                    annotations.append({
                        'tier_id': tier_id,
                        'annotation_id': ann_id,
                        'start_time': time_slots[ts_ref1],
                        'end_time': time_slots[ts_ref2],
                        'annotation_value': annotation_value
                    })

    return annotations

Folder setting¶

In [4]:
annofolder = curfolder + '/MT_annotated/'
autoannofiles = glob.glob(annofolder + '/*ELAN_tiers.eaf')
interfolder = curfolder + '/InterAg/'

print(autoannofiles)

manualannofiles_r1 = glob.glob(curfolder + '/ManualAnno/R1/*ELAN_tiers.eaf') # ola
manualannofiles_r3 = glob.glob(curfolder + '/ManualAnno/R3/*ELAN_tiers.eaf') # gillian
['e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_0_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_11_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_12_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_13_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_15_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_2_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_39_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_3_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_49_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_4_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_6_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_8_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_9_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_tpose_0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_tpose_1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_19_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_30_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_70_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_71_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_72_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_73_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_74_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_77_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_7_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_80_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_82_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_85_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_86_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_87_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_88_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_8_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_9_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_10_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_14_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_16_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_17_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_18_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_19_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_1_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_20_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_21_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_22_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_23_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_24_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_25_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_26_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_27_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_28_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_29_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_30_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_31_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_32_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_33_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_35_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_36_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_37_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_38_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_40_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_41_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_42_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_43_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_44_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_45_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_46_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_47_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_48_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_50_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_51_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_52_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_53_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_5_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_7_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_0_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_100_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_101_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_102_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_103_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_104_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_105_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_106_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_107_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_108_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_109_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_10_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_110_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_111_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_112_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_113_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_11_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_12_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_13_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_14_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_15_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_16_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_17_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_18_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_1_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_20_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_21_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_22_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_23_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_24_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_25_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_26_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_27_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_28_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_29_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_2_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_31_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_32_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_33_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_34_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_35_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_36_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_37_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_38_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_39_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_3_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_40_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_41_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_43_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_44_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_45_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_46_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_47_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_48_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_49_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_4_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_50_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_51_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_52_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_53_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_54_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_55_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_56_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_57_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_58_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_59_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_5_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_60_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_61_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_62_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_63_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_64_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_65_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_67_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_68_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_69_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_6_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_75_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_76_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_78_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_79_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_81_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_83_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_84_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_89_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_90_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_91_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_92_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_93_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_94_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_95_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_96_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_97_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_98_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_99_p1_ELAN_tiers.eaf']

TXT files, automatic anno¶

In [333]:
# empty txtfiles
txtfile_auto_mov =  interfolder + 'AutoAnno_mov.txt'
txtfile_auto_head = interfolder + 'AutoAnno_head.txt'
txtfile_auto_upper = interfolder + 'AutoAnno_upper.txt'
txtfile_auto_arms = interfolder + 'AutoAnno_arms.txt'
txtfile_auto_lower = interfolder + 'AutoAnno_lower.txt'

with open(txtfile_auto_mov, 'w') as f:
    for file in autoannofiles:
        print('working on ' + file)
        filename = file.split('\\')[-1]
        annotator = 'Anno_R2'
        annotations = parse_eaf_file(file, 'movement_in_trial')
        for annotation in annotations:
            print(annotation)
            f.write(f"{annotator}\t{annotation['start_time']}\t{annotation['end_time']}\t{annotation['annotation_value']}\t{filename}\n")

with open(txtfile_auto_head, 'w') as f:
    for file in autoannofiles:
        print('working on ' + file)
        filename = file.split('\\')[-1]
        annotator = 'Anno_R2'
        annotations = parse_eaf_file(file, 'head_mov')
        for annotation in annotations:
            f.write(f"{annotator}\t{annotation['start_time']}\t{annotation['end_time']}\t{annotation['annotation_value']}\t{filename}\n")

with open(txtfile_auto_upper, 'w') as f:
    for file in autoannofiles:
        print('working on ' + file)
        filename = file.split('\\')[-1]
        annotator = 'Anno_R2'
        annotations = parse_eaf_file(file, 'upper_body')
        for annotation in annotations:
            f.write(f"{annotator}\t{annotation['start_time']}\t{annotation['end_time']}\t{annotation['annotation_value']}\t{filename}\n")

with open(txtfile_auto_arms, 'w') as f:
    for file in autoannofiles:
        print('working on ' + file)
        filename = file.split('\\')[-1]
        annotator = 'Anno_R2'
        annotations = parse_eaf_file(file, 'arms')
        for annotation in annotations:
            f.write(f"{annotator}\t{annotation['start_time']}\t{annotation['end_time']}\t{annotation['annotation_value']}\t{filename}\n")

with open(txtfile_auto_lower, 'w') as f:
    for file in autoannofiles:
        print('working on ' + file)
        filename = file.split('\\')[-1]
        annotator = 'Anno_R2'
        annotations = parse_eaf_file(file, 'lower_body')
        for annotation in annotations:
            f.write(f"{annotator}\t{annotation['start_time']}\t{annotation['end_time']}\t{annotation['annotation_value']}\t{filename}\n")
['e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_0_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_11_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_12_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_13_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_15_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_2_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_39_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_3_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_49_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_4_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_6_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_8_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_9_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_tpose_0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_tpose_1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_19_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_30_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_70_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_71_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_72_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_73_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_74_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_77_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_7_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_80_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_82_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_85_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_86_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_87_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_88_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_8_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_9_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_1_10_p1_ELAN_tiers.eaf', 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'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_22_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_23_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_24_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_25_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_26_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_27_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_28_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_29_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_2_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_31_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_32_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_33_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_34_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_35_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_36_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_37_p1_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_38_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_39_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_3_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_40_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_41_p0_ELAN_tiers.eaf', 'e:\\FLESH_ContinuousBodilyEffort\\TS_processing/MT_annotated\\0_2_43_p0_ELAN_tiers.eaf', 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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_26_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_27_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_28_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_29_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_30_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_31_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_32_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_33_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_35_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_36_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_37_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_38_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_40_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_41_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_42_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_43_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_44_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_45_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_46_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_47_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_48_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_50_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_51_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_52_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_53_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_5_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_7_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_0_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_100_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_101_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_102_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_103_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_104_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_105_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_106_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_107_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_108_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_109_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_10_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_110_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_111_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_112_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_113_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_11_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_12_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_13_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_14_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_15_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_16_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_17_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_18_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_1_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_20_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_21_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_22_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_23_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_24_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_25_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_26_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_27_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_28_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_29_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_2_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_31_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_32_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_33_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_34_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_35_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_38_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_39_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_40_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_41_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_43_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_44_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_45_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_46_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_47_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_48_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_49_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_4_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_50_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_51_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_52_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_53_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_54_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_55_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_56_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_57_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_58_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_59_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_5_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_60_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_61_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_62_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_63_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_64_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_65_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_67_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_68_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_69_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_6_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_75_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_76_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_78_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_79_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_81_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_83_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_84_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_89_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_90_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_91_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_92_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_93_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_94_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_95_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_96_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_97_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_98_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_99_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_1_0_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_10_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_11_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_12_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_13_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_14_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_15_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_16_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_17_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_18_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_1_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_20_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_21_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_22_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_23_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_24_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_25_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_26_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_27_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_28_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_29_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_2_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_31_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_32_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_34_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_39_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_3_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_40_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_41_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_43_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_44_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_45_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_46_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_47_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_48_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_49_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_4_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_50_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_51_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_52_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_53_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_54_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_56_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_57_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_58_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_59_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_63_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_64_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_65_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_68_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_69_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_6_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_75_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_76_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_78_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_79_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_81_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_83_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_84_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_89_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_90_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_91_p0_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_92_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_93_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_94_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_95_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_96_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_97_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_98_p1_ELAN_tiers.eaf
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/MT_annotated\0_2_99_p1_ELAN_tiers.eaf

TXT files, manual anno¶

In [11]:
# define which folder you want to use
foi = manualannofiles_r3
raterIDfile = 'R3'
raterID = 'R2'

# IA files
txtfile_mov = interfolder + raterIDfile + '_Manual_mov.txt'
txtfile_head = interfolder + raterIDfile + '_Manual_head.txt'
txtfile_upper = interfolder + raterIDfile + '_Manual_upper.txt'
txtfile_lower = interfolder + raterIDfile + '_Manual_lower.txt'
txtfile_arms = interfolder + raterIDfile + '_Manual_arms.txt'


with open(txtfile_mov, 'w') as f:
    for file in foi:
        print('working on ' + file)
        # get the filename as the last element
        filename = file.split('\\')[-1]
        annotator = 'Anno_' + raterID  # no matter what ID, for easydiag it always has to be agreement between R1 and R2
        # parse the file
        annotations = parse_eaf_file(file, 'movement_in_trial')
        print(annotations)
        # write the annotations
        for annotation in annotations:
            f.write(f"{annotator}\t{annotation['start_time']}\t{annotation['end_time']}\t{annotation['annotation_value']}\t{filename}\n")

with open(txtfile_head, 'w') as f:
    for file in foi:
        print('working on ' + file)
        # get the filename as the last element
        filename = file.split('\\')[-1]
        # parse the file
        annotations = parse_eaf_file(file, 'head_mov')
        print(annotations)
        # write the annotations
        for annotation in annotations:
            f.write(f"{annotator}\t{annotation['start_time']}\t{annotation['end_time']}\t{annotation['annotation_value']}\t{filename}\n")

with open(txtfile_upper, 'w') as f:
    for file in foi:
        print('working on ' + file)
        # get the filename as the last element
        filename = file.split('\\')[-1]
        annotator = 'Anno_' + raterID
        # parse the file
        annotations = parse_eaf_file(file, 'upper_body')
        print(annotations)
        # write the annotations
        for annotation in annotations:
            f.write(f"{annotator}\t{annotation['start_time']}\t{annotation['end_time']}\t{annotation['annotation_value']}\t{filename}\n")

with open(txtfile_lower, 'w') as f:
    for file in foi:
        print('working on ' + file)
        # get the filename as the last element
        filename = file.split('\\')[-1]
        annotator = 'Anno_' + raterID
        # parse the file
        annotations = parse_eaf_file(file, 'lower_body')
        print(annotations)
        # write the annotations
        for annotation in annotations:
            f.write(f"{annotator}\t{annotation['start_time']}\t{annotation['end_time']}\t{annotation['annotation_value']}\t{filename}\n")

with open(txtfile_arms, 'w') as f:
    for file in foi:
        print('working on ' + file)
        # get the filename as the last element
        filename = file.split('\\')[-1]
        annotator = 'Anno_' + raterID
        # parse the file
        annotations = parse_eaf_file(file, 'arms')
        print(annotations)
        # write the annotations
        for annotation in annotations:
            f.write(f"{annotator}\t{annotation['start_time']}\t{annotation['end_time']}\t{annotation['annotation_value']}\t{filename}\n")
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_11_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A3380>
[{'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '3116', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_12_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A1080>
[{'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '3629', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_13_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A3380>
[{'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '3388', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_14_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A1080>
[{'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '5120', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_15_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A3380>
[{'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '3978', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_16_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A1080>
[{'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '3524', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_17_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A33D0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A1170>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8B80>
[{'tier_id': 'movement_in_trial', 'annotation_id': 'a5', 'start_time': '1560', 'end_time': '3310', 'annotation_value': 'movement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '1560', 'annotation_value': 'nomovement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '3310', 'end_time': '4263', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_20_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A0C20>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33E06D9E0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8900>
[{'tier_id': 'movement_in_trial', 'annotation_id': 'a4', 'start_time': '620', 'end_time': '3760', 'annotation_value': 'movement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '620', 'annotation_value': 'nomovement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '3760', 'end_time': '3881', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_21_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A32E0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8900>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8950>
[{'tier_id': 'movement_in_trial', 'annotation_id': 'a4', 'start_time': '610', 'end_time': '3514', 'annotation_value': 'movement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '610', 'annotation_value': 'nomovement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '3514', 'end_time': '3595', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_22_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A0D10>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A1120>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A1CB0>
[{'tier_id': 'movement_in_trial', 'annotation_id': 'a2', 'start_time': '540', 'end_time': '4499', 'annotation_value': 'movement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '540', 'annotation_value': 'nomovement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '4499', 'end_time': '4575', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_23_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A36A0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8D10>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8540>
[{'tier_id': 'movement_in_trial', 'annotation_id': 'a4', 'start_time': '980', 'end_time': '3734', 'annotation_value': 'movement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '980', 'annotation_value': 'nomovement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '3734', 'end_time': '3844', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_24_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A3790>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A32E0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A3650>
[{'tier_id': 'movement_in_trial', 'annotation_id': 'a2', 'start_time': '490', 'end_time': '5558', 'annotation_value': 'movement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '490', 'annotation_value': 'nomovement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '5558', 'end_time': '5699', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_25_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A1170>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B84F0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8B30>
[{'tier_id': 'movement_in_trial', 'annotation_id': 'a4', 'start_time': '650', 'end_time': '6089', 'annotation_value': 'movement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '650', 'annotation_value': 'nomovement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '6089', 'end_time': '6158', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_26_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A33D0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A36A0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6A3650>
[{'tier_id': 'movement_in_trial', 'annotation_id': 'a2', 'start_time': '920', 'end_time': '3890', 'annotation_value': 'movement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '0', 'end_time': '920', 'annotation_value': 'nomovement'}, {'tier_id': 'movement_in_trial', 'annotation_id': None, 'start_time': '3890', 'end_time': '4138', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_29_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8450>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9080>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B84A0>
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_11_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_24_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_26_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_29_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_2_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_30_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_31_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_32_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_33_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_34_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_35_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_38_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_39_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_3_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_40_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_41_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_42_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_43_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_44_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_47_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_48_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_49_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_4_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_50_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_51_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_52_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_53_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_5_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_6_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_7_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_8_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_100_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_101_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_102_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_103_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_104_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_105_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_106_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_107_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_108_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_109_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_10_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_110_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_111_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_112_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_113_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_11_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_12_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_13_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_14_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_15_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_16_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_17_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_18_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_23_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_24_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_25_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_26_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_27_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_28_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_29_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_2_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_30_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_31_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_32_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_33_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_34_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_35_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_36_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_37_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_3_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_41_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_42_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_43_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_44_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_45_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_46_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_47_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_48_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_49_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_4_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_50_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_51_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_52_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_57_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_58_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_59_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_5_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_60_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_61_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_62_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_63_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_64_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_65_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_66_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_6_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_73_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_74_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_75_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_76_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_77_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_78_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_79_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_7_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_80_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_81_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_82_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_83_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_84_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_85_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_86_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_87_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_88_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_89_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_8_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_91_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_98_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_99_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_11_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_12_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_13_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_14_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_15_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_16_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_17_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_20_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_21_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_22_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_23_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_24_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_25_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_26_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_29_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_2_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_30_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_31_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_32_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_33_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_34_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_35_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_38_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_39_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_3_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_40_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_41_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_42_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_43_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_44_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_47_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_48_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_49_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_4_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_50_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_51_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_101_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_17_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_23_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_24_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_25_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_26_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_27_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_28_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_29_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_3_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_42_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_43_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_44_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_45_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_46_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_47_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_48_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_49_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_4_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_50_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_51_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_52_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_57_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_58_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_59_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_5_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_60_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_61_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_62_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_63_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_64_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_65_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_66_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_6_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_87_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_8_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_91_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_98_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_99_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_21_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_32_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_34_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_35_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_38_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_51_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_52_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_1_6_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_13_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_14_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_16_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_17_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_24_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_25_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_27_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_28_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_29_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_2_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_30_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_31_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_32_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_33_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_34_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_35_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_36_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_37_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_3_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_41_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_42_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_43_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_44_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_45_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_46_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_47_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_48_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_49_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_4_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_50_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_51_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_52_p0_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_57_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_58_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_59_p1_ELAN_tiers.eaf
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working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_5_p0_ELAN_tiers.eaf
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<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9C60>
[{'tier_id': 'arms', 'annotation_id': 'a9', 'start_time': '550', 'end_time': '2519', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '550', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '2519', 'end_time': '2993', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_60_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9670>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8EA0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9B70>
[{'tier_id': 'arms', 'annotation_id': 'a5', 'start_time': '880', 'end_time': '3440', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '880', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '3440', 'end_time': '3441', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_61_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9DF0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B85E0>
[{'tier_id': 'arms', 'annotation_id': 'a4', 'start_time': '880', 'end_time': '3058', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '880', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_62_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9260>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8DB0>
[{'tier_id': 'arms', 'annotation_id': 'a4', 'start_time': '800', 'end_time': '5380', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '800', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_63_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B87C0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8FE0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9440>
[{'tier_id': 'arms', 'annotation_id': 'a4', 'start_time': '1380', 'end_time': '7303', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '1380', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '7303', 'end_time': '7304', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_64_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B92B0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9FD0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8900>
[{'tier_id': 'arms', 'annotation_id': 'a4', 'start_time': '2290', 'end_time': '6969', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '2290', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '6969', 'end_time': '7697', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_65_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9030>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8FE0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9260>
[{'tier_id': 'arms', 'annotation_id': 'a4', 'start_time': '710', 'end_time': '2999', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '710', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '2999', 'end_time': '4313', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_66_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8CC0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8A40>
[{'tier_id': 'arms', 'annotation_id': 'a4', 'start_time': '660', 'end_time': '6381', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '660', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_6_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8A90>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9350>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8680>
[{'tier_id': 'arms', 'annotation_id': 'a6', 'start_time': '680', 'end_time': '4960', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '680', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '4960', 'end_time': '5288', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_73_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8C20>
[{'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '5329', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_74_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B92B0>
[{'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '5033', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_75_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9EE0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9080>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8F90>
[{'tier_id': 'arms', 'annotation_id': 'a7', 'start_time': '450', 'end_time': '5240', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '450', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '5240', 'end_time': '5326', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_76_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9D50>
[{'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '3729', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_77_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B89F0>
[{'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '4539', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_78_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B99E0>
[{'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '4288', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_79_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9A80>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9350>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9F30>
[{'tier_id': 'arms', 'annotation_id': 'a5', 'start_time': '320', 'end_time': '4160', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '320', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '4160', 'end_time': '4226', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_7_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9A80>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9C60>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8EF0>
[{'tier_id': 'arms', 'annotation_id': 'a6', 'start_time': '1440', 'end_time': '7120', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '1440', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '7120', 'end_time': '7873', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_80_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9850>
[{'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '3400', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_81_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9D00>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B93F0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B95D0>
[{'tier_id': 'arms', 'annotation_id': 'a5', 'start_time': '250', 'end_time': '2339', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '250', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '2339', 'end_time': '4132', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_82_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9EE0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9BC0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B93F0>
[{'tier_id': 'arms', 'annotation_id': 'a5', 'start_time': '1120', 'end_time': '4329', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '1120', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '4329', 'end_time': '5280', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_83_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8AE0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8630>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8E00>
[{'tier_id': 'arms', 'annotation_id': 'a5', 'start_time': '1629', 'end_time': '4869', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '1629', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '4869', 'end_time': '5548', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_84_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8630>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8AE0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B90D0>
[{'tier_id': 'arms', 'annotation_id': 'a5', 'start_time': '1640', 'end_time': '4880', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '1640', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '4880', 'end_time': '5045', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_85_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8C70>
[{'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '4142', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_86_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9300>
[{'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '3705', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_87_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8D60>
[{'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '3689', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_88_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9F80>
[{'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '2928', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_89_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8D60>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B86D0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9DF0>
[{'tier_id': 'arms', 'annotation_id': 'a6', 'start_time': '3080', 'end_time': '5280', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '3080', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '5280', 'end_time': '5549', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_8_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8810>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9710>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B89A0>
[{'tier_id': 'arms', 'annotation_id': 'a4', 'start_time': '250', 'end_time': '2290', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '250', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '2290', 'end_time': '3148', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_90_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9170>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8590>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6BA020>
[{'tier_id': 'arms', 'annotation_id': 'a6', 'start_time': '570', 'end_time': '2929', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '570', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '2929', 'end_time': '3909', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_91_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9BC0>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9C60>
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8810>
[{'tier_id': 'arms', 'annotation_id': 'a6', 'start_time': '840', 'end_time': '2590', 'annotation_value': 'movement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '840', 'annotation_value': 'nomovement'}, {'tier_id': 'arms', 'annotation_id': None, 'start_time': '2590', 'end_time': '3149', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_98_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B8D60>
[{'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '5395', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_99_p1_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B9BC0>
[{'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '6185', 'annotation_value': 'nomovement'}]
working on e:\FLESH_ContinuousBodilyEffort\TS_processing/ManualAnno/R3\0_2_9_p0_ELAN_tiers.eaf
<Element 'ALIGNABLE_ANNOTATION' at 0x000001B33F6B89F0>
[{'tier_id': 'arms', 'annotation_id': None, 'start_time': '0', 'end_time': '3890', 'annotation_value': 'nomovement'}]

Merge txt files for EasyDIAG¶

In [12]:
# open AutoAnno_arms.txt and ManualAnno_arms.txt
# check which files are in the ManualAnno_arms.txt but not in AutoAnno_arms.txt
# get rows that contains these files from both txt files into one txt file IA_arms

toi = ['arms', 'head', 'upper', 'lower', 'mov']

for tier in toi:
    print('working on ' + tier)
    txtfile_auto = interfolder + 'AutoAnno_' + tier + '.txt'
    txtfile_manual_r1 = interfolder + 'R1_Manual_' + tier + '.txt'
    txtfile_manual_r3 = interfolder + 'R3_Manual_' + tier + '.txt'

    comp1 = 'R1' # change here who you want to compare
    comp2 = 'R3'   # with whom

    #read the files
    r1_anno = pd.read_csv(txtfile_manual_r1, sep='\t', header=None) # change here who you want to compare
    r2_anno = pd.read_csv(txtfile_manual_r3, sep='\t', header=None)    # with whom

    #get the files that are in manual_arms but not in auto_arms
    files_to_check_r1 = r1_anno[4].unique()
    files_to_check_r2 = r2_anno[4].unique()

    # create a list that contains files that are in both lists
    files_to_check = list(set(files_to_check_r1) & set(files_to_check_r2))

    # put away those that have 0_1_34, 0_2_42, and 0_2_66 in them - these are faulty trials in pose2sim so we dont have auto anno for them
    files_to_check = [x for x in files_to_check if '0_1_34' not in x and '0_2_42' not in x and '0_2_66' not in x]

    # get the rows that contain these files from auto_arms
    rows_auto = r1_anno[r1_anno[4].isin(files_to_check)]
    rows_manual = r2_anno[r2_anno[4].isin(files_to_check)]

    #concat rows_to_check_auto with manual_arms
    concat_rows = pd.concat([rows_auto, rows_manual])

    #save the rows to a new txt file
    txtfile_IA_arms = interfolder + 'IA_' + comp1 + '_' + comp2 + '_' + tier + '.txt'

    with open(txtfile_IA_arms, 'w') as f:
        for index, row in concat_rows.iterrows():
            f.write(f"{row[0]}\t{row[1]}\t{row[2]}\t{row[3]}\t{row[4]}\n")
working on arms
working on head
working on upper
working on lower
working on mov